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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 1–50 of 56 papers

TitleStatusHype
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation LearningCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
Check It Again: Progressive Visual Question Answering via Visual EntailmentCode1
Check It Again:Progressive Visual Question Answering via Visual EntailmentCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
Distilled Dual-Encoder Model for Vision-Language UnderstandingCode1
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart CaptioningCode1
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical AlignmentCode1
Fine-Grained Visual EntailmentCode1
Good Questions Help Zero-Shot Image ReasoningCode1
Harnessing the Power of Multi-Task Pretraining for Ground-Truth Level Natural Language ExplanationsCode1
How Much Can CLIP Benefit Vision-and-Language Tasks?Code1
I Can't Believe There's No Images! Learning Visual Tasks Using only Language SupervisionCode1
I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual MetaphorsCode1
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity RecognitionCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
MixGen: A New Multi-Modal Data AugmentationCode1
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal FusionCode1
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
VEglue: Testing Visual Entailment Systems via Object-Aligned Joint ErasingCode0
Prompt Tuning for Generative Multimodal Pretrained ModelsCode0
Visual Entailment: A Novel Task for Fine-Grained Image UnderstandingCode0
Visual Entailment Task for Visually-Grounded Language LearningCode0
Stop Pre-Training: Adapt Visual-Language Models to Unseen LanguagesCode0
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
Playing Lottery Tickets with Vision and Language—0
Pre-training image-language transformers for open-vocabulary tasks—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training—0
Few-shot Multimodal Multitask Multilingual Learning—0
Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing—0
How Much Can CLIP Benefit Vision-and-Language Tasks?—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation—0
"Let's not Quote out of Context": Unified Vision-Language Pretraining for Context Assisted Image Captioning—0
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