SOTAVerified

Image Captioning

Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.

( Image credit: Reflective Decoding Network for Image Captioning, ICCV'19)

Papers

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Showing 51–100 of 1878 papers

TitleStatusHype
SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical ImagingCode1
A Survey on Efficient Vision-Language ModelsCode1
AeroLite: Tag-Guided Lightweight Generation of Aerial Image Captions—0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
AstroLLaVA: towards the unification of astronomical data and natural language—0
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions—0
Impact of Language Guidance: A Reproducibility Study—0
How Can Objects Help Video-Language Understanding?—0
OmniCaptioner: One Captioner to Rule Them AllCode2
RS-RAG: Bridging Remote Sensing Imagery and Comprehensive Knowledge with a Multi-Modal Dataset and Retrieval-Augmented Generation Model—0
MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories—0
Group-based Distinctive Image Captioning with Memory Difference Encoding and Attention—0
A Conformal Risk Control Framework for Granular Word Assessment and Uncertainty Calibration of CLIPScore Quality Estimates—0
Context-Independent OCR with Multimodal LLMs: Effects of Image Resolution and Visual Complexity—0
Semantic-Spatial Feature Fusion with Dynamic Graph Refinement for Remote Sensing Image Captioning—0
JEEM: Vision-Language Understanding in Four Arabic Dialects—0
Unified Multimodal Discrete DiffusionCode2
Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy—0
Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language ModelsCode1
Improved Alignment of Modalities in Large Vision Language Models—0
Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation—0
Natural Language Generation—0
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report GenerationCode0
Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future PerspectivesCode1
Disentangling Fine-Tuning from Pre-Training in Visual Captioning with Hybrid Markov LogicCode0
Unified Autoregressive Visual Generation and Understanding with Continuous Tokens—0
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era—0
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing—0
Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic CognitionCode1
Falcon: A Remote Sensing Vision-Language Foundation ModelCode3
RONA: Pragmatically Diverse Image Captioning with Coherence RelationsCode0
Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification—0
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models—0
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment—0
ComicsPAP: understanding comic strips by picking the correct panel—0
Measuring directional bias amplification in image captions using predictability—0
Improving cognitive diagnostics in pathology: a deep learning approach for augmenting perceptional understanding of histopathology images—0
PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training—0
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models—0
Treble Counterfactual VLMs: A Causal Approach to HallucinationCode0
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language ModelCode2
A Benchmark for Multi-Lingual Vision-Language Learning in Remote Sensing Image CaptioningCode0
AC-Lite : A Lightweight Image Captioning Model for Low-Resource Assamese Language—0
Group Relative Policy Optimization for Image CaptioningCode0
Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models—0
Are Large Language Models Good Data Preprocessors?—0
Benchmarking Retrieval-Augmented Generation in Multi-Modal ContextsCode2
Fine-Grained Video Captioning through Scene Graph Consolidation—0
Good Representation, Better Explanation: Role of Convolutional Neural Networks in Transformer-Based Remote Sensing Image Captioning—0
Weakly Supervised Video Scene Graph Generation via Natural Language SupervisionCode1
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All datasetsVizWiz 2020 test-devCOCO Captionsnocaps in-domainnocaps near-domainnocaps out-of-domainnocaps entireCOCO (Common Objects in Context)VizWiz 2020 testnocaps-XD entirenocaps-val-in-domainnocaps-val-overallnocaps-XD in-domain

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1IBM Research AICIDEr80.67—Unverified
2CASIA_IVACIDEr79.15—Unverified
3feixiangCIDEr77.31—Unverified
4wocaoCIDEr77.21—Unverified
5lamiwab172CIDEr75.93—Unverified
6RUC_AIM3CIDEr73.52—Unverified
7funasCIDEr73.51—Unverified
8SRC-B_VCLabCIDEr73.47—Unverified
9spartaCIDEr73.41—Unverified
10x-vizCIDEr73.26—Unverified
#ModelMetricClaimedVerifiedStatus
1VALORCIDER152.5—Unverified
2VASTCIDER149—Unverified
3Virtex (ResNet-101)CIDER94—Unverified
4KOSMOS-1 (1.6B) (zero-shot)CIDER84.7—Unverified
5BLIP-FuseCapCLIPScore78.5—Unverified
6mPLUGBLEU-446.5—Unverified
7OFABLEU-444.9—Unverified
8GITBLEU-444.1—Unverified
9BLIP-2 ViT-G OPT 2.7B (zero-shot)BLEU-443.7—Unverified
10BLIP-2 ViT-G OPT 6.7B (zero-shot)BLEU-443.5—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLICIDEr149.1—Unverified
2GIT2, Single ModelCIDEr124.18—Unverified
3GIT, Single ModelCIDEr122.4—Unverified
4PaLICIDEr121.09—Unverified
5CoCa - Google BrainCIDEr117.9—Unverified
6Microsoft Cognitive Services teamCIDEr112.82—Unverified
7Single ModelCIDEr108.98—Unverified
8GRIT (zero-shot, no VL pretraining, no CBS)CIDEr105.9—Unverified
9FudanFVLCIDEr104.9—Unverified
10FudanWYZCIDEr104.25—Unverified
#ModelMetricClaimedVerifiedStatus
1GIT2, Single ModelCIDEr125.51—Unverified
2PaLICIDEr124.35—Unverified
3GIT, Single ModelCIDEr123.92—Unverified
4CoCa - Google BrainCIDEr120.73—Unverified
5Microsoft Cognitive Services teamCIDEr115.54—Unverified
6Single ModelCIDEr110.76—Unverified
7FudanFVLCIDEr109.33—Unverified
8FudanWYZCIDEr108.04—Unverified
9IEDA-LABCIDEr100.15—Unverified
10firetheholeCIDEr99.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLICIDEr126.67—Unverified
2GIT2, Single ModelCIDEr122.27—Unverified
3GIT, Single ModelCIDEr122.04—Unverified
4CoCa - Google BrainCIDEr121.69—Unverified
5Microsoft Cognitive Services teamCIDEr110.14—Unverified
6Single ModelCIDEr109.49—Unverified
7FudanFVLCIDEr106.55—Unverified
8FudanWYZCIDEr103.75—Unverified
9HumanCIDEr91.62—Unverified
10firetheholeCIDEr88.54—Unverified