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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 31–40 of 56 papers

TitleStatusHype
p-Laplacian Adaptation for Generative Pre-trained Vision-Language ModelsCode0
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
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks—0
A survey on knowledge-enhanced multimodal learning—0
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks—0
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks—0
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
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