Visual Commonsense Reasoning
Leaderboards
Greatest papers with code
From Recognition to Cognition: Visual Commonsense Reasoning
CVPR 2019
• rowanz/r2c
• 
While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world.
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
NeurIPS 2019
• jiasenlu/vilbert_beta
•
We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language.
IMAGE RETRIEVAL QUESTION ANSWERING VISUAL COMMONSENSE REASONING VISUAL QUESTION ANSWERING
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
NeurIPS 2019
• jiasenlu/vilbert_beta
•
We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language.
IMAGE RETRIEVAL QUESTION ANSWERING VISUAL COMMONSENSE REASONING VISUAL QUESTION ANSWERING
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
• jackroos/VL-BERT
•
We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short).
LANGUAGE MODELLING QUESTION ANSWERING VISUAL COMMONSENSE REASONING VISUAL QUESTION ANSWERING
UNITER: Learning UNiversal Image-TExt Representations
• ChenRocks/UNITER
•
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding.
LANGUAGE MODELLING QUESTION ANSWERING TEXT MATCHING VISUAL COMMONSENSE REASONING VISUAL QUESTION ANSWERING
Heterogeneous Graph Learning for Visual Commonsense Reasoning
NeurIPS 2019
• yuweijiang/HGL-pytorch
•
Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement.
Heterogeneous Graph Learning for Visual Commonsense Reasoning
NeurIPS 2019
• yuweijiang/HGL-pytorch
•
Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement.
TAB-VCR: Tags and Attributes based VCR Baselines
NeurIPS 2019
• Deanplayerljx/tab-vcr
•
Despite impressive recent progress that has been reported on tasks that necessitate reasoning, such as visual question answering and visual dialog, models often exploit biases in datasets.
QUESTION ANSWERING VISUAL COMMONSENSE REASONING VISUAL DIALOG VISUAL QUESTION ANSWERING
Connective Cognition Network for Directional Visual Commonsense Reasoning
Inspired by this idea, towards VCR, we propose a connective cognition network (CCN) to dynamically reorganize the visual neuron connectivity that is contextualized by the meaning of questions and answers.