SOTAVerified

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
How Much Can CLIP Benefit Vision-and-Language Tasks?Code1
Check It Again: Progressive Visual Question Answering via Visual EntailmentCode1
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation LearningCode1
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
UNITER: UNiversal Image-TExt Representation LearningCode1
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks—0
VEglue: Testing Visual Entailment Systems via Object-Aligned Joint ErasingCode0
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks—0
p-Laplacian Adaptation for Generative Pre-trained Vision-Language ModelsCode0
Lightweight In-Context Tuning for Multimodal Unified Models—0
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