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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
Stop Pre-Training: Adapt Visual-Language Models to Unseen LanguagesCode0
"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Few-shot Multimodal Multitask Multilingual Learning—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
A survey on knowledge-enhanced multimodal learning—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
Pre-training image-language transformers for open-vocabulary tasks—0
Prompt Tuning for Generative Multimodal Pretrained ModelsCode0
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language ExplanationsCode0
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