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Visual Entailment

Visual Entailment (VE) - is a task consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks. The goal is to predict whether the image semantically entails the text.

Papers

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Showing 21–30 of 56 papers

TitleStatusHype
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
Advancing Grounded Multimodal Named Entity Recognition via LLM-Based Reformulation and Box-Based SegmentationCode1
UNITER: UNiversal Image-TExt Representation LearningCode1
Understanding Figurative Meaning through Explainable Visual EntailmentCode1
Visual Spatial ReasoningCode1
p-Laplacian Adaptation for Generative Pre-trained Vision-Language ModelsCode0
Stop Pre-Training: Adapt Visual-Language Models to Unseen LanguagesCode0
VEglue: Testing Visual Entailment Systems via Object-Aligned Joint ErasingCode0
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language ExplanationsCode0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
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