Image Captioning
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Benchmarks
VizWiz 2020 test-dev
COCO Captions
nocaps in-domain
nocaps near-domain
nocaps out-of-domain
nocaps entire
VizWiz 2020 test
nocaps-XD entire
TextCaps 2020
nocaps-XD in-domain
nocaps-XD near-domain
nocaps-XD out-of-domain
nocaps-val-in-domain
nocaps-val-overall
nocaps-val-near-domain
nocaps-val-out-domain
SCICAP
Flickr30k Captions test
WHOOPS!
MS-COCO
Object HalBench
nocaps val
COCO Captions test
Conceptual Captions
FlickrStyle10K
Localized Narratives
AIC-ICC
BanglaLekhaImageCaptions
ChEBI-20
IU X-Ray
MSCOCO
Peir Gross
Most implemented
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Show and Tell: A Neural Image Caption Generator
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Self-critical Sequence Training for Image Captioning
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
Papers
ViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models
Gender bias in large vision-language models (LVLMs) undermines their fairness and reliability, compromising output trustworthiness. Current mitigation methods rely on training-phase adjustments or post-hoc calibration, b…
Visual GroundingImage CaptioningText GenerationSAVOR: Self-Aware Visual Grounding via Confidence-Calibrated Reinforcement Learning for Multimodal Hallucination Mitigation
Multimodal large language models (MLLMs) have made strong progress on visual question answering and image captioning, yet they still produce fluent claims about objects, attributes, or relations that are not grounded in …
Visual Question AnsweringReinforcement LearningVisual GroundingImage CaptioningFLAT: Resampling Image and Text into 1D Flexible-Length Aligned Transmodal Tokens for Retrieval and Generation
Traditional multimodal representation learning and generation are two stages: a contrastive or self-supervised visual encoder is trained first, followed by a separate downstream generative model. This setup bottlenecks g…
Representation LearningCross-Modal RetrievalImage CaptioningA Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss
An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-language models produce fluent high-level captions, yet routinely miss the attributes, counts, textures, materia…
Image CaptioningMapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding
In many low-resource settings, even just eliciting speech for data collection is difficult. One promising approach has been to ask speakers to describe images. But how do we build models from such visually grounded speec…
Visual GroundingImage CaptioningKeyword SpottingOverview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models
In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (\textbf{S}hared-task on \textbf{H}allucinations and \textbf{R}elated \textbf{O}bservable \textbf{O}vergeneration \textbf{M}istakes i…
Image CaptioningText Generation