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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
Visual Entailment Task for Visually-Grounded Language Learning—0
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering—0
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
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