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A Few Guidelines for Incremental Few-Shot Segmentation
Reducing the amount of supervision required by neural networks is especi...
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Attribute Prototype Network for Zero-Shot Learning
From the beginning of zero-shot learning research, visual attributes hav...
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Towards Recognizing Unseen Categories in Unseen Domains
Current deep visual recognition systems suffer from severe performance d...
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Generalized Many-Way Few-Shot Video Classification
Few-shot learning methods operate in low data regimes. The aim is to lea...
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Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
Weakly-supervised object localization (WSOL) has gained popularity over ...
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Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-based Image Retrieval
Low-shot sketch-based image retrieval is an emerging task in computer vi...
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Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring
Numerous car accidents are caused by improper driving maneuvers. Serious...
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e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
The recently proposed SNLI-VE corpus for recognising visual-textual enta...
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Learning Robust Representations via Multi-View Information Bottleneck
The information bottleneck principle provides an information-theoretic m...
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Evaluating Weakly Supervised Object Localization Methods Right
Weakly-supervised object localization (WSOL) has gained popularity over ...
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Understanding Misclassifications by Attributes
In this paper, we aim to understand and explain the decisions of deep ne...
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Modeling Conceptual Understanding in Image Reference Games
An agent who interacts with a wide population of other agents needs to b...
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Bayesian Zero-Shot Learning
Object classes that surround us have a natural tendency to emerge at var...
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Relational Generalized Few-Shot Learning
Transferring learned models to novel tasks is a challenging problem, par...
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Combining Generative and Discriminative Models for Hybrid Inference
A graphical model is a structured representation of the data generating ...
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Interpreting Adversarial Examples with Attributes
Deep computer vision systems being vulnerable to imperceptible and caref...
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f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
When labeled training data is scarce, a promising data augmentation appr...
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Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval
Zero-shot sketch-based image retrieval (SBIR) is an emerging task in com...
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XOC: Explainable Observer-Classifier for Explainable Binary Decisions
When deep neural networks optimize highly complex functions, it is not a...
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Visual Rationalizations in Deep Reinforcement Learning for Atari Games
Due to the capability of deep learning to perform well in high dimension...
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Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Many approaches in generalized zero-shot learning rely on cross-modal ma...
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Manipulating Attributes of Natural Scenes via Hallucination
In this study, we explore building a two-stage framework for enabling us...
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Textual Explanations for Self-Driving Vehicles
Deep neural perception and control networks have become key components o...
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Grounding Visual Explanations
Existing visual explanation generating agents learn to fluently justify ...
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Generating Counterfactual Explanations with Natural Language
Natural language explanations of deep neural network decisions provide a...
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Primal-Dual Wasserstein GAN
We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for b...
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Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Deep models that are both effective and explainable are desirable in man...
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Feature Generating Networks for Zero-Shot Learning
Suffering from the extreme training data imbalance between seen and unse...
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Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
Deep models are the defacto standard in visual decision problems due to ...
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Grounding Visual Explanations (Extended Abstract)
Existing models which generate textual explanations enforce task relevan...
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Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
Due to the importance of zero-shot learning, i.e. classifying images whe...
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Channel-Recurrent Variational Autoencoders
Variational Autoencoder (VAE) is an efficient framework in modeling natu...
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Zero-Shot Learning - The Good, the Bad and the Ugly
Due to the importance of zero-shot learning, the number of proposed appr...
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Exploiting saliency for object segmentation from image level labels
There have been remarkable improvements in the semantic labelling task i...
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Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Deep models are the defacto standard in visual decision models due to th...
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Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
Automatic image synthesis research has been rapidly growing with deep ne...
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Gaze Embeddings for Zero-Shot Image Classification
Zero-shot image classification using auxiliary information, such as attr...
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Learning What and Where to Draw
Generative Adversarial Networks (GANs) have recently demonstrated the ca...
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Generative Adversarial Text to Image Synthesis
Automatic synthesis of realistic images from text would be interesting a...
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Latent Embeddings for Zero-shot Classification
We present a novel latent embedding model for learning a compatibility f...
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Multi-Cue Zero-Shot Learning with Strong Supervision
Scaling up visual category recognition to large numbers of classes remai...
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Generating Visual Explanations
Clearly explaining a rationale for a classification decision to an end-u...
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Label-Embedding for Image Classification
Attributes act as intermediate representations that enable parameter sha...
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Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the...
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