In many of the most recent tasks tackled by artificial intelligence, multiple sources of information need to be taken into account for decision making. Problems such as visual question answering[Goyal et al.2017], visual relationship detection [Lu et al.2016], cross-modal retrieval [Kiros, Salakhutdinov, and Zemel2015] or social-media post classification [Duong, Lebret, and Aberer2017] require, at a certain level and to some extent, the fusion between multiple modalities.
For classical mono-modal tasks, linear models constitute a handful building block to transform the input and to match it with the desired output . When dealing with two modalities, not only do we need to properly transform each input and into a representation that fits the problem, we also want to model the interactions between these modalities. A natural candidate to extend linear models for two inputs are bilinear models. However, the number of parameters of a bilinear model is quadratic in the input dimensions: as a linear model is characterized by a matrix , a bilinear model is defined by a tensor . When the input dimensions grow (thousand or more), learning a full tensor becomes quickly intractable. The main issue is to reduce and control the numbers of parameters representing . Usually, when working with linear models, restricting the complexity is done by constraining the rank of the matrix . Unfortunately, it is much more complicated when it comes to bilinear models, since it requires notions of multi-linear algebra. While a lot of work has been done in extending the notion of complexity to higher-order tensors [Harshman et al.2001], [Carroll and Chang1970], [Tucker1966], it is not clear whether the simple extension of rank should be used to constrain a bilinear model.
In this paper, we tackle the general problem of learning end-to-end bilinear models. We propose BLOCK, a Block Superdiagonal Fusion framework for multimodal representation based on the block-term tensor decomposition [De Lathauwer2008]. As it has been studied in the signal processing literature [Cichocki et al.2015]
, the focus was on having uniqueness properties to ensure a physically interpretable decomposition. We study here this decomposition under a machine learning perspective instead, and use it as a fully learnable tensor of parameters. Interestingly, this decomposition leverages the notion of block-term ranks to define a tensor’s complexity. It encapsulates both concepts ofrank and mode ranks, at the basis of Candecomp/PARAFAC [Harshman et al.2001] and Tucker decomposition [Tucker1966]. This complexity analysis is capitalized on to provides a new way to control the tradeoff between the expressiveness and complexity of the fusion model. More precisely, BLOCK enables to model very rich (i.e. full bilinear) interactions between groups of features, while the block structure limits the whole complexity of the model, which enables to keep expressive (i.e. high dimensional) mono-moodal representations.
The BLOCK model is used for solving two challenging applications: Visual Question Answering (VQA) and Visual Relationship Detection (VRD). For both tasks, the number of blocks and the size of each projection in the BLOCK fusion will be adapted to balance between fine interaction modeling and low number of parameters. We embed our bilinear BLOCK fusion strategy into deep learning architectures ; through extensive experiments, we validate the relevance of the approach as we provide an extensive and systemic comparison of many state-of-the-art multimodal fusion techniques. Moreover, we obtain very competitive results on three commonly used datasets: VQA 2.0 [Goyal et al.2017], TDIUC [Kafle and Kanan2017] and the VRD dataset [Lu et al.2016].
2 BLOCK fusion model
In this section, we present our BLOCK fusion strategy and discuss its connection to other bilinear fusion methods from the literature.
A bilinear model takes as input two vectorsand , and projects them to a K-dimensional space with tensor products:
where . Each component of is a quadratic form of the inputs: ,
A bilinear model is completely defined by its associated tensor , the same way as a linear model is defined by its associated matrix.
2.1 BLOCK model
In order to reduce the number of parameters and constrain the model’s complexity, we express using the block-term decomposition. More precisely, the decomposition of in rank (L,M,N) terms is defined as:
where , , and . This decomposition is called block-term because it can be written as
where (same for and ), and the block-superdiagonal tensor of , as illustrated in Figure 1. Applying this structural constraint to in Eq. (1), we can express with respect to and . Let and . These two projections are merged with a fusion parametrized by the block-superdiagonal tensor . Each block in this tensor merges together chunks of size from and of size from to produce a vector of size :
where is a vector of dimension containing the corresponding values in . Finally, all the vectors are concatenated to produce . The final prediction vector is .
To further reduce the number of parameters in the model, we add a constraint on the rank of each third order slices matrices of the blocks , as it was done in some recent VQA applications (see Section 3).
When working with a linear model, a usual technique to restrict the hypothesis space and number of parameters is to constrain the rank of its associated matrix. The formal notion of matrix rank quantifies how complex a linear model is allowed to be. However, when it comes to restricting the complexity of a bilinear model, multiple algebraic concepts can be used. We give two examples that are related to the block-term decomposition.
where denotes the outer product, and the vectors , and represent the elements of the decomposition. The rank of is defined by the minimal number of triplet vectors so that the Eq. (6) is true. Thus, restricting the hypothesis space for to the set of tensors defined by Eq. (6) guarantees that the rank is upper-bounded by .
Applying this constraint on , Eq. (1) is simplified into
where , and , and denotes element-wise product. This decompositioncan be seen as a special case of the block-term decomposition where , reducing to a super-diagonal identity tensor.
Another way to restrict a three-way tensor’s complexity is through its mode ranks. The rank-(L,M,N) Tucker decomposition [Tucker1966] of is defined as
where , , and . This decomposition assumes a constraint on the three unfolding matrices of , such that , and (following the notations in [De Lathauwer2008]).
Applying this constraint to , Eq. (1) can be re-written as:
This decomposition can be seen as a special case of the block-term decomposition where there is only block in the core tensor.
As was studied by [De Lathauwer2008], the notion of tensor complexity should be expressed not only in terms of rank or mode ranks, but using the number of blocks and the mode-n ranks of each block. It appears that CP and Tucker decompositions are two extreme cases, where only one of the two quantities is used. For the CP, the number of blocks corresponds to the rank, but each block is of size (1,1,1). The monomodal projections can be high-dimensional and thus integrate rich transformation of the input, but the interactions between both projections is relatively poor as a dimension from one space is only allowed to interact with another. For the Tucker decomposition, there is only one block of size (L,M,N). The interaction modeling is very rich since all inter-correlations between feature dimensions of the different modalities are considered. However, this quantity of possible interactions limits the dimensions of the projected space, which can cause a bottleneck in the model.
BLOCK being built on the block-term decomposition, we constrain the tensor using a combination of both concepts, which provides a richer modeling of the interactions between modalities. This richness is ensured by the tensors , each parametrizing a bilinear function that takes as inputs chunks of and . As this interaction modelling is done by chunks and not for every possible combination of components in and , we can reach high dimensions in the projections and without exploding the number of parameters in . This property of having a fine interaction modeling between high dimensional projections is very desirable in our context where we need to model complex interactions between high-level semantic spaces. As we show in the experiments, performance of a bilinear model strongly depends on both the number and the size of the blocks that parametrize the system.
3 BLOCK fusion for VQA task
The task of Visual Question Answering [Antol et al.2015], [Goyal et al.2017] has been a fertile playground for researchers to investigate on how bilinear models should be used. In the classical setup for VQA, shown in Figure 2, image and question have to be merged with a multimodal fusion technique, which can be implemented as an instance of bilinear model. The answer is predicted through a classification layer that follows the fusion module. In MCB [Fukui et al.2016], the bilinear interaction is simplified using a sketching technique. However, more recent techniques tackle this complexity issue from a tensor decompositions standpoint: MLB [Kim et al.2017] and MUTAN [Ben-Younes et al.2017] constrain the tensor of parameters using respectively the CP and Tucker decomposition. In MFB [Yu et al.2017b], the tensor is viewed as a stack of matrices, and a classical matrix rank constraint is imposed on each of them. Finally in MFH [Yu et al.2018], multiple MFB blocks are cascaded to model higher-order interactions between inputs. Most recent techniques embed some of these fusion strategies into more elaborated architecture that involve multiple types of visual features [Jiang et al.2018], or specific modules [Zhang, Hare, and Prügel-Bennett2018] designed for precise question types. In this section, we compare BLOCK to the other multimodal fusion techniques, and show how it surpasses them both in terms of performance and number of parameters.
3.1 VQA architecture
Our VQA model is based on a classical attentional architecture [Fukui et al.2016], enriched by our proposed merging scheme. Our fusion model is shown in Figure 2. We use the Bottom-up image features provided by [Teney et al.2018], consisting of a set of detected objects and their representation (see [Mordan et al.2017, Durand et al.2017] for further insights on detection and localization). To get a vector embedding of the question, words are preprocessed and then fed into a pretrained Skip-thought encoder [Kiros et al.2015]. The outputs of this language model are used to produce a single vector representing the whole question, as in [Yu et al.2018]. We use a BLOCK fusion to merge the question and image representations. The question vector is used as a context to guide the visual attention. Saliency scores are produced using a BLOCK fusion between each image vector and the question embedding.
Details: For the BLOCK layers, we set
and constrain the rank of each mode-3 slices of each block to be less than 10. We found these hyperparameters with a a cross-validation on theval set. As in [Yu et al.2018], we consider the 3000 most frequent answers. As in [Ben-Younes et al.2017], we use a cross-entropy loss with answer sampling. We jointly optimize the parameters of our VQA model using Adam [Kingma and Ba2015] with a learning rate of
, without learning rate decay or gradient clipping, and with a batch size of 200. We early stop the training of our models according to their accuracy on a holdout set.
3.2 Fusion analysis
In Table 1, we compare BLOCK to 8 different fusion schemes available in the literature on the commonly used VQA 2.0 Dataset [Goyal et al.2017]. This dataset is composed of 658,111 image-question-answers triplets for training and validation (trainval set), and 447,793 triplets for evaluation (test-std set). We train on trainval minus a small subset used for early-stopping, and report the performance on test-dev set. For each fusion strategy, we run a grid search over its hyperparameters and keep the model that performs best on our validation set. We report the size of the model, corresponding to the number of parameters between the attended image features, the question embedding, and the answer prediction. We briefly describe the different fusion schemes used for the comparison:
– (1) the two vectors are projected on a common space, and their summation is projected to predict the answer;
– (2) the vectors are concatenated and passed at the input of a 3-layer MLP;
– (3) a bilinear interaction based on a count-sketching technique that projects the outer product of between inputs on a multimodal space;
– (4) a bilinear interaction where the tensor is expressed as a Tucker decomposition;
– (5) a bilinear interaction where the tensor is expressed as a CP decomposition;
– (6) a bilinear interaction where each 3rd mode slice matrix of the tensor is constrained by its rank;
– (7) a bilinear interaction where the tensor is expressed as a Tucker decomposition, and where its core tensor has the same rank constraint as (6);
– (8) a higher order fusion composed of cascaded (6);
– (9) our BLOCK fusion.
From the results in Table 1, we see that the simple sum fusion (1) provides a very low baseline. We also note that the MLP (2) doesn’t provide the best results, despite its non-linear structure. As the MLP should be able to find that two different modalities are used and that it needs to look for interactions between them, this is in practice difficult to obtain. Instead, top performing methods are based on a bilinear model. The structure imposed on the parameters highly influences the final performance. We can see that (3), which simplifies the bilinear model using random projections, has efficiency issues due to the count-sketching technique. These issues are alleviated in the other bilinear methods, which use the tensor decomposition framework to practically implement the interaction. Our BLOCK method (9) gives the best results. As we saw, the block-term decomposition generalizes both CP and Tucker decompositions, which is why it is not surprising to see it surpass them. Moreover, the fact that it integrates the 3rd order slices rank constraint gives it the advantages of (6) and (7). Interestingly, it even surpasses (8) which is based on a higher-order interaction modeling, while using 30M less parameters. This strongly indicates that controlling a bilinear model through its block-term ranks provides an efficient trade-off between modeling capacities and number of parameters. To further validate this hypothesis, we evaluate a BLOCK fusion with only 3M parameters. This model obtained 64.91%. Unsurprisingly, it does not surpasses all the methods against which we compare. However, it obtains competitive results, improving over 5 out of 8 methods that all use far more parameters.
|(3)||B + count-sketching||MCB [Fukui et al.2016]||32M||61.23||79.73||39.13||50.45|
|(4)||B + Tucker decomp.||Tucker [Ben-Younes et al.2017]||14M||64.21||81.81||42.28||54.17|
|(5)||B + CP decomp.||MLB [Kim et al.2017]||16M||64.88||81.34||43.75||53.48|
|(6)||B + low-rank on the 3rd mode slices||MFB [Yu et al.2017a]||24M||65.56||82.35||41.54||56.74|
|(7)||Combination of (4) and (6)||MUTAN [Ben-Younes et al.2017]||14M||65.19||82.22||42.1||55.94|
|(8)||Higher order fusion||MFH [Yu et al.2018]||48M||65.72||82.82||40.39||56.94|
|(9)||B + Block-term decomposition||BLOCK||18M||66.41||82.86||44.76||57.3|
3.3 Comparison to leading VQA methods
. On this more recent dataset, evaluation metrics are provided to assess the robustness of the model with respect to answer imbalance, as well as to account for performance homogeneity across the difference question types.
|Most common answer [Kafle and Kanan2017]||51.15||31.11||17.53||15.63||0.83|
|Question only [Kafle and Kanan2017]||62.74||39.31||25.93||21.46||8.42|
|NMN* [Andreas et al.2016]||79.56||62.59||51.87||34.00||16.67|
|MCB* [Fukui et al.2016]||81.86||67.90||60.47||42.24||27.28|
|RAU* [Noh and Han2016]||84.26||67.81||59.00||41.04||23.99|
As we show in Table 2, our model is able to outperform the preceding ones on TDIUC by a large margin for every metrics, especially those which account for bias in the data. We notably report a gain of +1.7 in accuracy, +3.95 in A-MPT, +5.05 in H-MPT, +16.12 in A-NMPT, +15.45 in H-NMPT, over the best scoring model in each metric. The high results in the harmonic metrics (H-MPT and H-NMPT) suggest that BLOCK performs well across all question types, while the high scores in the normalized metrics (A-NMPT and H-NMPT) denote that our model is robust to answer imbalance type of bias in the dataset.
In Table 3, we see that our fusion model obtains competitive results on VQA 2.0 compared to previously published methods. As we are outperformed by [Zhang, Hare, and Prügel-Bennett2018], whose proposition rely on a completely different architecture, we believe that both our contributions are orthogonal. Still, our model performs better than [Teney et al.2018] and [Yu et al.2018], with whom we share the global VQA architecture. In further details, we point out that BLOCK surpasses [Yu et al.2018] reaching a +1.78 improvement in the overall accuracy on test-dev, even though the latter encompasses the current state-of-the-art fusion scheme. Furthermore, we use the same image features than [Teney et al.2018] and are able to achieve a +2.26 gain on test-dev and +2.25 on test-std.
|Model||VQA2 Test-dev||VQA2 Test-std|
|Most common answer [Goyal et al.2017]||-||-||-||-||25.98||61.20||0.36||1.17|
|Question only [Goyal et al.2017]||-||-||-||-||44.26||67.01||31.55||27.37|
|Deep LSTM* [Lu et al.2015]||-||-||-||-||54.22||73.46||35.18||41.83|
|MCB* [Fukui et al.2016]||-||-||-||-||62.27||78.82||38.28||53.36|
|ReasonNet [Ilievski and Feng2017]||-||-||-||-||64.61||78.86||41.98||57.39|
|TipsAndTricks [Teney et al.2018]||65.32||81.82||44.21||56.05||65.67||82.20||43.90||56.26|
|MFH [Yu et al.2018]||65.80||-||-||-||-||-||-||-|
|Counter [Zhang, Hare, and Prügel-Bennett2018]||68.09||83.14||51.62||58.97||68.41||83.56||51.39||59.11|
4 VRD task
The task of Visual Relationship Detection aims at predicting triplets of the type ”subject-predicate-object” where subject and object are localized objects, and predicate is a label corresponding to the relationship that links them (for example: ”man-riding-bicycle”, ”woman-holding-phone”). To predict this relationship, multiple types of information are available, for both the subject and object regions: classes, bounding box coordinates, visual features, etc. However, this context being more recent than VQA, fusion techniques are less formalized and more ad-hoc. In [Hanwang Zhang2017], the relation is predicted by a substractive fusion between subject and object representations, each consisting in a linear function of relative coordinates, class distributions and visual features. [Li et al.2017] predicts the relationship by a complex message passing structure between subject and object representations, and [Dai, Zhang, and Lin2017] uses a formulation inspired from Conditional Random Fields to perform joint recognition between the subject, object and predicate classes. We adopt in the following a very simple architecture, to put emphasis on the fusion module between different information sources.
4.1 VRD Architecture
Our VRD architecture is shown in Figure 3. It takes as inputs a subject and an object bounding box. Each of them is represented as their 4-dimensional box spatial coordinates and (normalized between 0 and 1), their object classes and , and their semantic visual features and . To predict the relationship predicate, we use one fusion module for each type of features following Eq. (10).
where can be implemented as BLOCK, or any other multimodal fusion. Each fusion module outputs a vector of dimension , all concatenated into a 3d-dimensional vector that will serve as an input to a linear layer predictor . The system is trained with back-propagation on a binary-crossentropy loss.
An other important component is the object detector. As usually done, we first train a Faster-RCNN on the object boxes of the VRD dataset. For Predicate prediction, we use it as a features extractor given the ground truth bounding boxes. For Phrase detection and Relationship detection, we use it to extract the bounding boxes with their associated features.
For a system to perform well on Phrase and Relationship detection, it should have been also trained on pairs of (subject, object) boxes that are not linked together by any relationship. During training, we randomly sample half of all possible negative pairs, and assign them an all-zeros label vectors.
The VRD dataset [Lu et al.2016] is composed of 5,000 images with 100 object categories and 70 predicates. It contains 37,993 relationships with 6,672 unique triplets and an average of 24.25 predicates per object category. The dataset is divided between 4,000 images for training and 1,000 for testing. Three different settings are commonly used to evaluate a model on VRD: (1) Predicate prediction: the coordinates and class labels are given for both subject and object regions. This setup allows to assess the model’s ability to predict a relationship, regardless of the object detection stage. (2) Phrase detection: a predicted triplet subject, predicate, object matches a ground-truth if the three labels match and if the union region of its bounding boxes matches the union region of the ground-truth triplet, with IoU above 0.5. (3) Relationship detection: more challenging than (2), this one requires that both subject and object intersect with an IoU higher than 0.5. For each of these settings, performance is usually measured with Recall@50 and Recall@100
4.3 Fusion analysis
To show the effectiveness of the BLOCK bilinear fusion, we run the same type of experiment we did in the previous section. For each fusion technique, we use the architecture described in Equation 10 where we replace by the corresponding bilinear function. As we did for VQA, we cross-validate the hyperparameters of each fusion technique and keep the best model each time. In Table 4, we see that BLOCK still outperforms all previous methods on each of the three tasks. We can remark that for this task, the non linear MLP perform relatively well compared to the other methods. It is likely that an MLP can model the interactions at stake for VRD more easily than those for VQA. However, we can improve over this strong baseline using a BLOCK fusion.
|(3)||B + count-sketching||2M||82.23||89.07||13.42||15.8||9.17||10.79|
|(4)||B + Tucker decomp.||3M||83.25||89.77||11.23||14.09||7.37||9.00|
|(5)||B + CP decomp.||4M||85.96||91.66||23.67||26.50||16.41||18.59|
|(6)||B + low-rank on the 3rd mode slices||15M||85.21||91.06||25.31||28.03||17.83||19.77|
|(7)||Combination of (4) and (6)||30M||85.65||91.33||25.77||28.65||18.53||20.38|
|(8)||Higher order fusion||16M||85.58||91.3||26.09||28.73||18.81||20.63|
In the next experiments, we validate the power of our BLOCK fusion, and analyze how it behaves under different setups. We randomly split the training set into three train/val sets, and plot the mean and standard deviation of the recall@50 calculated over them. In Figure(a)a, we fix the dimension of the block-superdiagonal tensor to and vary the number of blocks used to fill this tensor. When , which corresponds to the Tucker decomposition, the number of parameters in the core tensor is equal to , making the system arduously trainable on our dataset. On the opposite, when , the number of parameters is controlled, but the mono-modal projections are only allowed to interact through an element-wise multiplication, which makes the interaction modeling relatively poor. The block-term decomposition provides an in-between working regime, reaching an optimum when .
In Figure (b)b, we keep the number of parameters fixed. As the number of chunks increases, the dimensions of the mono-modal projections also increases. Once again, an optimum is reached when . These results confirm our hypothesis that the way the parameters are distributed within the tensor, in terms of size and number of blocks, has a real impact on the system’s performance.
4.4 Comparison to leading VRD methods
|Yu et. al [Yu et al.2017a]||✓||85.64||94.65||26.32||29.43||22.68||31.89|
|Li et. al [Li et al.2017]||✗||-||-||22.78||27.91||17.32||20.01|
|Liang et. al [Liang, Lee, and Xing2017]||✗||-||-||21.37||22.60||18.19||20.79|
|Zhang et. al [Hanwang Zhang2017]||✗||44.76||44.76||19.42||22.42||14.07||15.20|
|Lu et. al [Lu et al.2016]||✗||47.87||47.87||16.17||17.03||13.86||14.70|
|Peyre et. al [Peyre et al.2017]||✗||52.6||52.6||17.9||19.5||15.8||17.1|
|Dai et. al [Dai, Zhang, and Lin2017]||✗||80.78||81.90||19.93||23.45||17.73||20.88|
In Table 5, we compare our system to the state-of-the-art methods on VRD. On predicate prediction, our fusion outperforms all previous methods on R@50, including [Yu et al.2017a] that uses external data. On R@100, the BLOCK fusion is only marginally outperformed by [Yu et al.2017a], but we perform better than all methods that don’t use extra data. These results validate the efficiency of the block-term decomposition to predict a predicate by fusing information coming from ground truth subject and object boxes. On phrase detection, our BLOCK fusion achieves better results than all previous models in R@50. Notably, the scores obtained for phrase detection are lower than for predicate prediction, since the ground truth regions are not provided in this setup. Finally, on relationship detection, BLOCK surpasses all previous methods without extra data in R@50, and gives similar performance than [Dai, Zhang, and Lin2017] in R@100. The scores for relationship detection are lower than for phrase detection: in this setup, a prediction is positive if both subject and object boxes match the ground truth. On contrary, in phrase detection, the comparison between prediction and ground truth is done on the union between subject and object regions. Lastly, unlike some of the methods reported in Table 5, we do not fine-tune or adapt the detection network to the visual relationship tasks.
In this work, we introduce BLOCK, a bilinear fusion model whose tensor of parameters is structured using the block-term decomposition. BLOCK aims at optimizing the tradeoff between complexity and modeling capacities, and combines the strengths of the CP and Tucker decompositions. It offers the possibility to model rich interactions between groups of features, while still using high-dimensional mono-modal representations.
We apply BLOCK for two challenging computer vision tasks: VQA and VRD, where the parameters of our BLOCK fusion model are learned. Comparative experiments show that BLOCK improves over previous fusion schemes including linear, bilinear and non-linear models. We also show that BLOCK is able to maintain competitive performances with very compact parametrization.
In future works, we plan to extend the BLOCK idea to other applications. In particular, we want to explore the use of multiple input and output modalities, and to apply BLOCK for interpreting and explaining the behaviour of the multimodal deep fusion model [Engilberge et al.2018, Carvalho et al.2018].
This work has been supported within the Labex SMART supported by French state funds managed by the ANR within the Investissements d’Avenir programme under reference ANR-11-LABX-65.
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