Papers Image Captioning
“Image Captioning” 태그가 달린 논문 2,086편 · 필터 해제
A 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 GenerationRe$^3$Cap: Retrieval-Guided Refinement for Image Captioning Enhancement via Reinforcement Learning
Reinforcement Learning (RL) has demonstrated significant gains in image captioning, yet it is still limited in encouraging Large Vision-Language Models (LVLMs) to explore novel reasoning strategies. This limitation leads…
Reinforcement LearningImage CaptioningIs Multimodal Speculative Decoding Ready for Diffusion-Based Parallel Drafting? A Survey and Empirical Diagnosis
Speculative decoding accelerates autoregressive generation by allowing a lightweight drafter to propose future tokens while a target model verifies them in parallel. Its lossless guarantee has motivated a line of work th…
Visual ReasoningImage CaptioningTowards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment
Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems. However, unlike general image captioning, clinically reliable …
Image CaptioningType predictionClinically Structured Surrogate Rewards for Post-SFT Medical Image Captioning
Medical image captioning requires translating heterogeneous visual evidence into concise clinical descriptions, where errors in findings, assertion states, or anatomical relations can alter clinical meaning despite surfa…
Image CaptioningEntity-Faithful Repair of Synthetic Supervision for Zero-Shot Image Captioning
Zero-shot image captioning aims to generate image descriptions without annotated image-text pairs. Recent approaches exploit text-to-image models to synthesize training data from text-only corpora, but most focus on impr…
Image CaptioningTowards Faithful Sentimental Image Captioning via Evidence-Aware Multi-Agent Reasoning
Sentimental Image Captioning (SIC) requires balancing emotional expression with visual fidelity. Existing methods often struggle with this trade-off, leading to hallucinations due to insufficient local grounding and the …
Image CaptioningA Reconstruction-Based Framework for Caption Evaluation Beyond Reference Captions
Image captioning is a primary task in vision--language research, yet assessing how faithfully a caption preserves image semantics without relying on reference captions remains unsettled. Prevailing evaluations rely on hu…
Image CaptioningLEMUR 2: Unlocking Neural Network Diversity for AI
Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain or deployment-aware evaluation. LEMUR 2 introduces a large-scale, ext…
Image CaptioningSeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering
Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual understanding tasks such as image captioning and visual question answering. However, they remain susceptible to hallucinations, generating …
Visual Question AnsweringFeature EngineeringImage CaptioningPaying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models
Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However, multi-role self-play and self-consistency reward schemes in existin…
Visual Question AnsweringImage CaptioningVisual ReasoningVisChronos: Revolutionizing Image Captioning Through Real-Life Events
This paper aims to bridge the semantic gap between visual content and natural language understanding by leveraging historical events in the real world as a source of knowledge for caption generation. We propose VisChrono…
Natural Language UnderstandingDense CaptioningImage CaptioningAre We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications
Multimodal Large Language Models (MLLMs) have redefined visual understanding by combining vision encoders with large-scale language models. This unified architecture enables strong performance on tasks like image caption…
Visual Question AnsweringImage CaptioningSAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis
Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions and lifestyle habits. However, early diagnosis remains limited by inadequ…
Multi-Label ClassificationMulti-class ClassificationVisual Question AnsweringImage CaptioningExtraction and Analysis of Multimodal Concepts in Vision Language Models through Sparse Autoencoders
Vision Language Models (VLMs) have demonstrated impressive performance in tasks requiring joint understanding of images and text, such as image captioning and Visual Question Answering (VQA), but our understanding of the…
Visual Question AnsweringImage CaptioningMIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning
Despite recent progress in Vision-Language Models (VLMs), mixed-domain image-caption datasets for both general-purpose and CCTV-based video surveillance systems remain limited. To address this gap, we introduce a large-s…
Object DetectionImage CaptioningHierarchical Multi-Modal Retrieval for Knowledge-Grounded News Image Captioning
Traditional image captioning methods often struggle to generate comprehensive, context-rich descriptions, especially for details not directly observable from visual cues. To overcome this, we propose a novel retrieval-au…
Image CaptioningCIAN: Multi-Stage Framework for Event-Enriched Image Captioning via Retrieval-Augmented Generation
Event-enriched image captioning describes not only visible content but also the broader context of events, including timing, location, and participants, capabilities missing in most pixel-bound models. We propose the Con…
Image Captioning