Audio-Visual Scene-Aware Dialog

01/25/2019 ∙ by Huda Alamri, et al. ∙ Georgia Institute of Technology MERL 48

We introduce the task of scene-aware dialog. Given a follow-up question in an ongoing dialog about a video, our goal is to generate a complete and natural response to a question given (a) an input video, and (b) the history of previous turns in the dialog. To succeed, agents must ground the semantics in the video and leverage contextual cues from the history of the dialog to answer the question. To benchmark this task, we introduce the Audio Visual Scene-Aware Dialog (AVSD) dataset. For each of more than 11,000 videos of human actions for the Charades dataset. Our dataset contains a dialog about the video, plus a final summary of the video by one of the dialog participants. We train several baseline systems for this task and evaluate the performance of the trained models using several qualitative and quantitative metrics. Our results indicate that the models must comprehend all the available inputs (video, audio, question and dialog history) to perform well on this dataset.

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1 Introduction

Developing conversational agents has been a longstanding goal of artificial intelligence (AI). Some recent research has focused on designing and training conversational agents (chatbots) that are visually grounded. Das

et al[6] introduced the problem of visual dialog, in which the task is to train a model to carry out a conversation in natural language about static images. Developing visually aware conversational agents is an emerging and vibrant area of research that promises to extend the capabilities of conversational agents. However, conversing about a static image is inherently limiting. Many potential applications for conversational agents, such as a helper robot or a smart home, would benefit greatly from understanding the scene in which the agent or a human is operating. The context often cannot be captured only by a still image, as there is important information in the temporal dynamics of the scene as well as in the audio. Our goal is to move towards chat agents that are not only visually intelligent but also aware of the sound and temporal dynamics. Such an AI agent can help answer questions such as the following: Is there any one coming to the door? Can you hear any noise in the room? When did they leave the house? Is my cat still eating? Answering such questions requires a holistic understanding of the visual and audio information in the scene, including its temporal dynamics.

Figure 1: In Audio Visual Scene-Aware Dialog, an agent’s task is to answer in natural language questions about a short video. The agent grounds its responses on the dynamic scene, the audio, and the history (previous rounds) of the dialog.

We introduce the task of scene-aware dialog, as well as a new Audio Visual Scene-aware Dialog (AVSD) Dataset to provide a means for training and testing scene-aware dialog systems. In the general task of scene-aware dialog, the goal of the system is to carry on a conversation with a human about a temporally varying scene, such as a video or a live scene. In the AVSD Dataset, we are addressing a particular type of scene-aware dialog. Each dialog in the dataset is a temporal sequence of question/answer (QA) pairs about a short video that includes human actions. We defined a specific task for the scene-aware dialog system to learn: Given an input video, the history (the first QA pairs) of a dialog about the video, and a follow-up question (the st question in the dialog), the system’s goal is to automatically generate complete and natural responses to the follow-up question.

We aim to use the dataset to explore the compositionality of dynamic scenes and train an end-to-end model to leverage information from the video frames, audio signals, and dialog history. The system should engage in this conversation by providing complete, natural responses to enable real-world applicability. The development of such scene-aware conversational agents represents an important frontier in artificial intelligence. In addition, it holds promise for numerous practical applications, such as retrieving video content from users’ free-form queries and helping visually impaired people understand visual content.

Our contributions include the following:

  1. [topsep=0pt, partopsep=0pt, itemsep=2pt, parsep=0pt, leftmargin=14pt]

  2. We introduce the task of scene-aware dialog, which is a multimodal semantic comprehension.

  3. We introduce a new benchmark for the scene-aware dialog task, the AVSD Dataset, consisting of more than 11,000 conversations that discuss the content (including actions, interactions, sound, and temporal dynamics) of videos of humans.

  4. We analyze the performance of several baseline systems on this new benchmark dataset.

2 Related Work

Video Datasets:

In the domain of dynamic scene understanding, there is a large body of literature focused on video action classification

[10, 9, 21, 37, 16]. Benchmarks like HMDB51 [20], Sports-1M [17], and UCF-101 [32]

have been widely used to demonstrate the performance of several machine learning models in the task of action recognition.

To target a broader range of action categories and handle a larger quantity of videos with more realistic settings, Caba Heilbron et al. introduced ActivityNet [3], a dataset of 280,000 YouTube videos with more than 200 different human action classes. Another benchmark for the task of human action recognition is Kinetics [18], which consists of 500,000 videos of around 400 action classes. Both ActivityNet and Kinetics are used in the ActivityNet Large Scale Activity Recognition Challenge[13]. Sigurdsson et al., presented the Charades dataset. Charades is a crowd-sourced video dataset that was built by asking Amazon Mechanical Turk (AMT) workers to write some scene scripts of daily activities, then asking another group of AMT workers to record themselves “acting out” the scripts in a “Hollywood style.” The dataset is also temporally annotated with a list of actions and objects in every temporal segment. [30] Video Captioning: Video captioning is the task of describing the dynamic scene with a natural sentence. The generated description should capture the semantic knowledge of the video, the objects and the actions. It also expresses the spatio-temporal relationship between the dynamics in the scene [29, 38, 12]. Several datasets have been introduced to benchmark the video captioning task [41, 19, 35, 15, 22].

Visual Question Answering: Inspired by the success of image-based question answering [1, 39, 11, 42], some recent work has addressed the task of video-based question answering [22]. MovieQA by Tapaswi M. et al. [35] focuses on story comprehension for text and video. MovieQA consists of 14,944 questions about 408 movies. MovieQA and TVQS are challenging benchmarks and have achieved promising results. However, they only focus on the problem of one question and answer. In AVSD we focus on the problem of multiple rounds of questions and answers. Another important point is that the questions and answers in these datasets are generated from the text associated with the movies. However in AVSD, the questions are initiated by a person who does not have access to the video or the text associated with it, resulting conversation that is not biased by the associated textual information.

VisDial: Our work is directly related to the image-based dialog (VisDial) introduced by Das et al. [6]. Given an input image, a dialog history, and a question, the agent is required to answer the given question while grounding the answer on the input image and the dialog history. The paper introduces several networks architectures to encode the different input modalities: late fusion LSTM, hierarchical LSTM and a memory network encoder. The model responses were modeled using generative and discriminative models. In this paper, we extend the work from [6] to include more complex modality: video frames and audio signals.

Dataset # Video Clips # QA Pairs Video Source
TVQA[22] 21,793 152,545 TV shows
MovieQA[35] 408 14,944 Movies
TGIF-QA[15] 56,720 103,919 Social media
VisDial[6] 120,000 (images) 1.2 M N/A
AVSD (Ours) 11,816 118,160 Crowdsourced
Table 1: Comparison with existing video question answering and visual dialog datasets.

3 Audio Visual Scene-Aware Dialog Dataset

A primary goal of our paper is to create a benchmark for the task of scene-aware dialog. There are several characteristics that we desire for such a dataset: 1) The dialogs should focus on the dynamic aspects of the video, actions and interactions; 2) The answers should tend toward more complete explanatory responses rather than brief one- and two-word answers (e.g., not simply yes or no); 3) The conversations should discuss the events in the video in their temporal order.

Table 1 puts the AVSD dataset in context with several other video question answering benchmarks. While AVSD has fewer unique video clips compared to TVQA and MovieQA, which are curated from television and film, our videos are more naturalistic. Moreover, AVSD contains a similar number of questions and answers, but as a part of multi-round dialogs.

Video Content. An essential element to collecting video-grounded dialogs is of course the videos themselves. We choose to collect dialogs grounded in the Charades [30] human-activity dataset. The Charades dataset consists of 11816 videos of daily indoor human activities with an average length of 30 seconds. Each video includes at least two actions. Examples of frames and action scripts for Charades videos are shown in Figure 2. We choose the Charades dataset for two main reasons. First, the videos in this dataset are crowd-sourced on Amazon Mechanical Turk (AMT), so the settings are natural and diverse. Second, each video consists of a sequence of small events that provide AMT Workers (Turkers) with rich content to discuss.

Figure 2: Examples of videos and scripts from the Charades dataset. Each video’s temporally ordered sequence of small events is a good fit for our goal to train a video-based dialog system.

3.1 Data Collection

We adapt the real-time chat interface from [6] to pair two AMT workers to have a conversation about a video from the Charades Dataset (Figure 2). One person, the “Answerer,” is presented with the video clip and the script, and their role is to provide detailed answers to questions about the scene. The other person, the “Questioner,” does not have access to the video or the script,and can only see three frames (one each from the beginning, middle, and end) of the video. The Questioner’s goal is to ask questions to obtain a good understanding of what happens in the video scene. We make several design choices in the data collection interface in order to encourage natural conversations about the activities in the videos.

Figure 3: Set of instructions for both AMT workers about their roles of “Questioner” and “Answerer”.

Investigating Events in Video. To help distinguish this task from previous image and video captioning tasks, our instructions direct the Questioner to “investigate what is happening” rather than simply asking the two Turkers to “chat about the video.” We find that when asked to “chat about the video,” Questioners tend to ask a lot of questions about the setting and the appearance of the people in the video. In contrast, the direction “investigate what is happening” leads Questioners to inquire more about the actions of the people in the video.

Seeding the Conversation. There are two reasons that our protocol provides the Questioners with three frames before the conversation starts: First, since the images provide the overall layout of the scene, they ensure that the conversations are centered around the actions and events that take place in the video rather than about the scene layout or the appearance of people and objects. Second, we found that providing multiple frames instead of a single frame encouraged users to ask about the succession of events. The ordering of the questions in the dialog follows the temporal events in the videos to a certain extent, (e.g., early questions in the dialog ask about events or actions in the first part of the video). Providing the Questioners with these three images achieves both criteria without explicitly dictating Questioners’ behavior; this is important because we want the conversations to be as natural as possible.

Downstream Task: Video Summarization. Once the conversation (sequence of 10 QA pairs) between the Questioner and Answerer is complete, the Questioners’ final task is to summarize what they think happened in the video. Knowing that this will be their final task motivates the Questioners to ask good questions that will lead to informative answers about the events in the video. In addition, this final downstream task is used to evaluate the quality of the dialog and how informative it was about the video. Figure 3 shows the list of instructions, and the examples provided to the help them complete the task.

Worker Qualifications. To ensure high-quality and fluent dialogs, we restrict our tasks on AMT to Turkers with task acceptance rates, located in North America, and having completed at least 500 tasks already. We further restrict any one Turker from completing more than 200 tasks in order to maintain diversity. In total, 1553 unique workers contributed to the dataset collection effort.

Figure 4: Example conversation between two AMT workers. The Questioner is presented with 3 static images from the video and asks a question. The Answerer, who has already watched the video and read the script, responds. Then the Questioner asks a follow-up question, the Answerer replies, and so on. After 10 rounds of QA, the Questioners provide a written summary of what they think happened in the video based on the conversation.

3.2 AVSD Dataset Analysis

(a) AVSD Answers
(b) AVSD Questions
(c) AVSD sentence lengths
Figure 5:

Distribution of first n-grams for AVSD Answers, And AVSD Questions. Distribution of lengths for AVSD questions and answers compared to VisDial.

In this section, we analyze the new AVSD V.1 Dataset. In total, the dataset contains 11,816 conversations (7985 training, 1863 validation, and 1968 testing), each including a video summary (written by the Questioner after each dialog). There are a total of 118,160 question/answer (QA) pairs. Figure 4 shows an example of our dataset.

We compare the length of AVSD questions and answers with those from VisDial [6] in Figure 4(a). As we can see, the answers and questions in AVSD are longer in average than in VisDial. The average length for AVSD questions is 7.85 words and the average answer length is 9.43. In contrast, VisDial questions average 5.12 words and are answered by 2.9 words answers on average. This shows that dialogs in our set are much more verbose and conversational.

Audio-Related Questions. When the Questioners were presented with 3 frames of the video, the AMT workers asked more questions about the audio track of the video, such as whether there was any music or noise, or whether the people were talking. In of the conversations, there are questions about the audio. Here are some examples of these audio-related questions from the dataset:

Does she appear to talk to anyone? Do you hear any noise in the background? Is there any music? Can you can hear him sneezing? Do the men talk to each other?

Moreover, looking at the burst diagram for questions in Figure 4(b) we can see questions like “Can / Do you hear …” and “Is there any sound …” appear frequently in the dataset.

Temporal Questions. Another common type of questions is about what happened next. As previously noted, the investigation of the temporal sequence of events was implicitly encouraged by our experimental protocol, such as providing the Questioner with three images from different parts of the video. In fact, people asked questions about what happened next in more that of the conversations. Here are some examples of such questions, taken from many different conversations:

Does he do anything after he throws the medicine away? Where does she lay the clothes after folding them? What does he do after locking the door? what does he do after taking a sip? Does he do anything after he sits on the stairs?

Likewise, we see questions like “What happens …” and “What does he do …” style questions occur frequently in the dataset as shown in Figure 4(b).

BLEU BLEU BLEU BLEU METEOR ROUGE CIDEr
video-watcher 0.638 0.433 0.287 0.191 0.223 0.407 0.429
Questioner 0.560 0.379 0.249 0.165 0.191 0.369 0.297
Table 2: Comparison on different metrics of a video-watcher summary vs the 3 other video-watcher summaries and the Questioner’ summary vs the 3 other video-watcher summaries.

Dataset quality. In order to further evaluate dialog quality, we develop and run another study where we ask AMT workers to watch and summarize the videos from the AVSD dataset. The instruction was "Summarize what is happening in the video". During the dialog data collection, the Questioner is asked, using the same instruction, to summarize the video based on the knowledge gathered through the conversation. We collect 4 summaries per video. We use the BLEU [26], ROUGE [23], METEOR [2] and CIDEr [36] metrics to compare the summaries collected from the video-watcher to the ones collected from the Questioners. In Table 2, the first row evaluates a randomly selected video-watcher summary vs. three others, and the second row evaluates the Questioner’s summary vs. the same three other video-watcher summaries. Both these numbers are close, demonstrating that the questioners do manage to gain an understanding of the video, similar to having watched it.

4 Model

Figure 6: An overview of our late-fusion multimodal network. The encoder takes each input modality and transforms them to a state embedding that is used to rank candidate answers.

To demonstrate the potential and the challenges of this new dataset, we design and analyze a video-dialog answerer model. The model takes as input a video, the audio track of the video, a dialog history (which comprises a ground-truth script or video caption followed by the first QA pairs of the dialog), and a follow-up question (the st question in the dialog). The model should ground the question in both the video and its audio and use the dialog history to leverage contextual information in order to answer.

Moving away from the hierarchical or memory network encoders common for dialog tasks [6], we opt to present a straightforward, discriminative late-fusion approach for scene-aware dialog that was recently shown to be effective for visual dialog [14]. This choice also enables a fair ablation study for the various input modalities, an important endeavour when introducing such a strongly multimodal task. For this class of model architecture, increases or decreases in performance from input ablation are directly linked to the usefulness of the input rather than to any complications introduced by the choice of network structure (e.g., some modalities having many more parameters than others).

An overview of our model is shown in Figure 6. At a high level, the network operates by fusing information from all of the modalities into a fixed sized representation then comparing this state with a set of candidate answers, selecting the most closely matching candidate as the output answer. In the rest of this section, we provide more details of the model and the input encodings for each modality.

Input Representations. The AVSD dataset is a challenging multimodal reasoning task including natural language, video, and audio. We describe how we represent each of these as inputs to the network. These correspond to the information that was available to the human Answerer in round of a dialog.

  • Video Caption (C): Each dialog in AVSD starts with a short natural language description (ground-truth script or caption) of the video contents.

  • Dialog History (DH): The dialog history consists of the initial caption (C) and each of the question-answer pairs from previous rounds of dialog. At round , we write the dialog history as . We concatenate the elements of the dialog history and encode them using an LSTM trained along with the late-fusion model.

  • Question (Q): The question to be answered, also known as . The question is encoded by an LSTM trained along with the late-fusion model.

  • Middle Frame (I): In some ablations, we represent videos using only their middle frame to eliminate all temporal information as a mean to evaluate the role of temporal visual reasoning. In these cases, we encode the frame using a pretrained VGG-16 network [31]

    that was trained on ImageNet 

    [7] .

  • Video (V): Each AVSD dialog is grounded in a video that depicts people performing simple actions. We transform the video frames into a fixed sized feature using the popular pretrained I3D model [4]. I3D is a 3D convolutional network that pushed state-of-the-art on multiple popular activity recognition tasks [20, 32]. To our knowledge, we are the first to explore the use of I3D for question answering in video-grounded dialog.

  • Audio (A): We similarly encode the audio from the video using a pretrained AENet model [34]. AENet is a convolutional audio encoding network that operates over long-time-span spectrograms, and it has been shown to improve activity recognition when combined with video features.

Encoder Network In order to combine the features encoded from these diverse inputs, we follow recent work in visually grounded dialog [14]: we simply concatenate the features and allow fusion to occur through fully-connected layers. More concretely, we can write our network’s computation as:

where , , , and

are the dialog history, question, video and audio feature embeddings as described above. The embeddings are concatenated to form the vector

which is passed through a linear layer with a tanh activation to form the joint embedding vector . For any of our ablations of these input modalities, we simply train a network excluding that input, without adjusting the linear layer output size.

Decoder Model We approach this problem as a discriminative ranking task, selecting an output from a set of candidate options, since these approaches have proven to be stronger than their generative counterparts in visual dialog [6]. (However, we note that generative variants need not rely on a fixed answer pool and may be more useful in general deployment.) More concretely, given a set of 100 potential answers , the agent learns to pick the most appropriate response.

The decoder computes the inner product between a candidate answer embedded with an LSTM and the holistic input embedding

generated by the encoder. We repeat this for all of the candidate answers, then pass the results through a softmax layer to compute probabilities of all of the candidates. At training time, we maximize the log-likelihood of the correct answer. At test time, we simply rank candidates according to their probabilities and select the argmax as the best response. We can write the decoder as:

(1)

where is the embedding vector for answer candidate , the notation represents an inner product, and is the score computed for the candidate based on its similarity to the input encoding . The vector contains scores for all of the candidates and passes through a softmax during training. The model is then trained with cross-entropy loss to score the ground-truth dialog candidate highly.

Selecting Candidate Answers: Following the selection process in [6], the set of 100 candidates answers consists of four types of answers: the ground-truth answer, hard negatives that are ground-truth answers to similar questions (but different video contexts), popular answers, and answers to random questions. We first sample 50 plausible answers which are the ground-truth answers to the 50 most similar questions. We are looking for questions that start with similar tri-grams (i.e., are of the same type such as “what did he”) and mention similar semantic concepts in the rest of the question. To accomplish this, all the questions are embedded in a common vector space. The question embedding is computed by concatenating the GloVe [27] embeddings of the first three words with the averaged GloVe embedding of the remaining words in the question. We then use Euclidean distance to select the closest neighbor questions to the original question. Those sampled answers are considered as hard negatives, because they correspond to similar questions that were asked in completely different contexts (different video, audio and dialog). In addition, we select the 30 most popular answers from the dataset. By adding popular answers, we force the network to distinguish between purely likely answers and plausible responses for the specific question, which increases the difficulty of the task. The next 19 candidate answers are sampled from the ground-truth answers to random questions in the dataset. The final candidate answer is the ground-truth (human-generated) answer from the original dialog.

Implementation details: Our implementation is based on the visual dialog challenge starter code [8]. The VisDial repository also provides code and model to extract image features. We extract video features using the I3D model [4]. The repository [28] provides code and models fine-tuned on the Charades dataset to extract video features. We subsample 40 frames from the original video and feed them into the rgb pipeline of the I3D model. The frames are sampled to be equally spaced in time. For the audio features, we use the AEnet network [34]. The repository [43] provides code to extract features from an audio signal. We first extract the audio track from the original Charades videos and convert them into 16kHz, 16bit, mono-channel signals. Both the video and audio features have the same dimension (4096).

5 Experiments

Data Splits. Recall from Section 3 that the AVSDv1.0 dataset contains 11k instances split across training (8k), validation (1.5k), and testing (1.5k) corresponding to the source Charades video splits. We present results on the test set.

Evaluation Metrics. Although metrics like BLEU [26], METEOR [2], and ROUGE [23] have been widely used to evaluate dialog [25, 33, 40], there has been recent evidence that they do not correlate well with human judgment [24]. In contrast, we do what [6] does. We instead choose to evaluate our models by checking individual responses at each round in a retrieval or multiple-choice setting. The agent is given a set of 100 answer candidates (generated as described in Section 4) and must select one. We report the following retrieval metrics:

  • Recall@k [higher is better] that measures how often the ground truth is ranked in the top choices

  • Mean rank (MR) [lower is better] of the ground truth answer which is sensitive to overall tendencies to rank ground-truth higher – important in our context as other candidate answers may be equally plausible

  • Mean reciprocal rank (MRR) [higher is better] of the ground truth answer which values placing ground truth in higher ranks more heavily

We note evaluation even in these retrieval settings for dialog has many open questions. One attractive alternative that we leave for future work is to evaluate directly with human users in cooperative tasks [5].

Figure 7: Example using Q+DH+V+A. The left column of the tables in each figure represents the corresponding answer probability.

6 Results and Analysis

In order to assess the challenges presented by the AVSDv1.0 dataset and the usefulness of different input modalities to address them, we present comprehensive ablations of our baseline model with respect to inputs. Table 3 reports the results of our models on AVSDv1.0 test. We find that our best performing models are those that can leverage video, audio, and dialog histories – signaling that the dialog collected in AVSD is grounded in multi-modal observations. In the rest of this section, we highlight noteworthy results.

Model MRR R@1 R@5 R@10 Mean
Language Only
Answer Prior 7.85 1.66 8.17 16.54 28.54
Q 36.12 20.01 53.72 74.55 7.63
Q + DH 50.40 32.76 73.27 88.60 4.72
Perception w/o Dialog Context
Q + I 35.12 19.08 52.36 73.35 7.90
Q + V 39.36 22.32 59.34 78.65 6.86
Q + A 35.94 19.46 54.55 75.14 7.58
Q + V + A 38.83 22.02 58.17 78.18 7.00
Q + C + I 36.77 20.30 55.28 75.72 7.44
Full Models
Q + DH + I 50.52 32.98 73.26 88.39 4.73
Q + DH + V 53.41 36.22 75.86 89.79 4.41
Q + DH + V + A 53.03 35.65 75.76 89.92 4.39
Table 3: Results of model ablations on the AVSDv1.0 test split. We report mean receiprocal rank (MRR - higher is better), recall@k (R@K - higher is better), and mean rank (Mean - lower is better). We find that our best performing model leverages the dialog, video, and audio signals in order to answer questions.

Language-only Baselines. The first three lines of Table 3 show the language-only models. First, the Answer Prior model encodes each answer with an LSTM and scores it against a static embedding vector learned over the entire training set. This model lacks question information, dialog history, or any form of perception, and acts as a measure of dataset answer bias. Naturally, it performs poorly over all metrics, though it does outperform chance. We also examine a question-only model Q that selects answers based only on the question encoding as well as a question and dialog Q+DH model that also includes the dialog history. These models measure regularities between questions or dialog and answer distributions. We find that access to the question greatly improves performance over the answer prior from 28.54 mean rank to 7.63 with question alone and the addition of the dialog improves this further to 4.72.

Dialog history is a strong signal. The dialog history appears to be a very strong signal – models with it consistently achieve mean ranks in the 4-4.8 range even without additional perception modalities whereas models without dialog history struggle to get below a mean rank of 7. This makes sense, we purposely designed our data collection task to generate dialog focusing on successions of action which naturally lead to follow up questions with answers that are strongly dependent on the prior conversation. We note that adding video and audio signals improves over just dialog – providing complementary information to ground questions.

Temporal perception seems to matter. Adding video features (V) consistently leads to improvements for all models. To further tease apart the effect of temporal perception from being able to see the scene in general, we run two ablations where rather than the video features, we encode visual perception using only the middle frame of the video. In both cases, Q+I and Q+DH+I, we see that the addition of static frames hurts performance marginally whereas addition of video features leads to improvements. It seems then that while temporal perception is helpful, models with access to just the middle image learn poorly generalizable groundings. We point out that one confounding factor for this finding is that the image is encoded with a VGG network rather than the I3D encoding for videos.

Audio provides a boost. The addition of audio features generally improves model performance (Q+V to Q+V+A being the exception). Interestingly, we see model performance even when combined with dialog history and video features (Q+DH+V+A) for some metrics, indicating there is still complementary knowledge between the video and audio signals despite their close relationship.

Audio and Temporal Based Questions. Table 4 shows mean rank on a subset of questions. We filter the questions using the two lists of keywords: audio related words {talk hear sound audio music noise} and temporal related words: {after, before, beginning, then, end, start}. We then generated answers to those questions using the three different models Q, Q+A and Q+V and compared which one would lead to higher rank of the ground truth answer.

Q Q+A Q+V
audio questions 6.91 6.69 6.52
temporal questions 7.31 7.15 5.98
Table 4: Mean rank results for the three models Q, Q+A and, Q+V for audio related questions and temporal related questions.

For the audio related questions we can see that although both the Q+A and Q+V outperform the Q model, the visual features seem more useful. This can be easily balanced as it is also unlikely that vision is unnecessary in audio questions. However, the temporal related questions shown to be better answered using the Q+V model which confirms our intuition. The Q+A helps only slightly ( vs ) but the Q+V yields to better improvements ( vs ).

Qualitative Examples. Figure 8 shows two examples using the setup Q+DH+V+A. The first column in the answer table of each figures is the answer probability. The ground truth answer is highlighted in red.

7 Conclusion

We introduce a new AI task: Audio Visual Scene-Aware Dialog, where the goal is to hold a dialog by answering a user’s questions about dynamic scenes in a natural language manner. We collected the Audio Visual Scene-Aware Dialog dataset through a two-person chat protocol on more than 11,000 videos of human actions. We also developed a model and experimented on many ablation studies to highlight the quality and complexity of the data collected. Our results show that the dataset is very rich having all the different modalities playing a role in tackling this task. We believe our dataset can serve in evaluating progress in audio and visual intelligence agents.

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Appendix Overview

This supplementary document is organized as follow:

  • Sec. H qualitative examples from AVSD.

  • Sec. I snapshots of our Amazon Mechanical Turk interface that served collecting the video summaries along with some examples.

H Qualitative examples from our dataset.

In this section, we discuss the model responses to several types of challenging and interesting questions in our dataset. In a video-based dialog, questions can be about audio, visual appearance, temporal information or actions. We examine the model responses for these question based on different input modalities, examples are randomly select from the test set.

h.1 Examples with Q+dh+v+a

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Figure 8: Examples using Q+DH+V+A. The left column of the tables in each figure represents the corresponding answer probability.

Figures 7(a) and 7(b) show examples of audio related questions. In figure 7(a), although model ranked the ground truth answer at the third position, the two top ranked answers can also be valid answers to the given question "Dose he say any thing?" . In 7(b), 3 out of the top 4 ranked answers can be a valid answers as well. They all answered ’no’ to the question. This highlights the deep understanding of the question and context. Figures 7(c), 7(d) and 7(e) are examples of visual-related questions. In figure 7(e), the model must determine a person’s age by leveraging visual cues from the video frames. An important type of questions in video-based dialog is the temporal-based question. Examples of this type are shown in Figure 7(d) and 7(f). Figures 7(g) and 7(h) show interesting and challenging questions about the general scene. In our dataset, there are no binary answers "yes" or "no", the Answers were asked to provide further details about their responses.

h.2 Examples comparing setups Q, Q+v, Q+a and Q+dh+v+a

Figure 9 shows examples comparing results between models Q, Q+V, Q+A and Q+DH+V+A. The GT rank is the rank of the ground truth answer for the corresponding model. The top answer is the first ranked answer for the corresponding model. The red highlights the best model. In figure 8(a) the question is audio related question and the Q+A model performs better. The question from the example in figure 8(b) is visual related question and the Q+V model performs best. Figure 8(c) presents a temporal related question best answered by the Q+V model. This highlights the value of each modality in the dataset.

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Figure 9: Comparison between models Q, Q+V, Q+A and Q+DH+V+A. The GT rank is the rank of the ground truth answer for the corresponding model. The top answer is the first ranked answer for the corresponding model. The red highlights the best model.

I Summaries Interface.

The data collection process of AVSD included a downstream task, where the Questioners had to write a summary of what they think happened in the video, based on the conversation they had about it. To evaluate the quality of these conversations, we ran a separate study case on AMT. We asked 4 people to watch the video and write a summary describing all the events in the video. Figure 10 shows the interface for this task. People where presented with example of the video and the script for that video. We then compared these summaries with the one written by the questioner.

Figure 11 shows some examples of the 4 summaries collected in this study (first four rows) and the summary written by the Questioner at the end of the dialog (last row). In these examples, we see that the summary written by the Questioner captures most of the events described in the 4 summaries.

Figure 10: Summaries data collection interface on AMT.
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Figure 11: Comparison between different video summaries. The first 4 rows are summaries written by people after watching the entire video. Last row is summary written by the questioner who did not watch the video.