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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 1451–1500 of 1878 papers

TitleStatusHype
MAT: A Multimodal Attentive Translator for Image Captioning—0
Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval—0
Measuring directional bias amplification in image captions using predictability—0
Measuring Machine Intelligence Through Visual Question Answering—0
Measuring Representational Harms in Image Captioning—0
MedBLIP: Fine-tuning BLIP for Medical Image Captioning—0
Medical Image Captioning via Generative Pretrained Transformers—0
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding—0
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification—0
Metropolis-Hastings Captioning Game: Knowledge Fusion of Vision Language Models via Decentralized Bayesian Inference—0
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information—0
Mindstorms in Natural Language-Based Societies of Mind—0
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding—0
Mitigating Image Captioning Hallucinations in Vision-Language Models—0
Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy—0
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment—0
MITRE at SemEval-2017 Task 1: Simple Semantic Similarity—0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
MMCFND: Multimodal Multilingual Caption-aware Fake News Detection for Low-resource Indic Languages—0
MM-PhyRLHF: Reinforcement Learning Framework for Multimodal Physics Question-Answering—0
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training—0
MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning—0
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection—0
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models—0
Module-wise Adaptive Distillation for Multimodality Foundation Models—0
Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks—0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—0
MORAL: A Multimodal Reinforcement Learning Framework for Decision Making in Autonomous Laboratories—0
More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data—0
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching—0
MRRC: Multiple Role Representation Crossover Interpretation for Image Captioning With R-CNN Feature Distribution Composition (FDC)—0
MSCap: Multi-Style Image Captioning With Unpaired Stylized Text—0
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language—0
Multi-Image Summarization: Textual Summary from a Set of Cohesive Images—0
Multi-layered Image Representation for Image Interpretation—0
Multi-Level Policy and Reward Reinforcement Learning for Image Captioning—0
Multimodal Attention for Neural Machine Translation—0
Multimodal Differential Network for Visual Question Generation—0
Multi-Modal Generative Embedding Model—0
Multimodal Image Captioning for Marketing Analysis—0
Multi-Modal Image Captioning for the Visually Impaired—0
Multimodal Learned Sparse Retrieval for Image Suggestion—0
Multimodal Learning: Are Captions All You Need?—0
Multimodal Machine Learning: Integrating Language, Vision and Speech—0
Multimodal Machine Translation through Visuals and Speech—0
Multimodal Named Entity Disambiguation for Noisy Social Media Posts—0
Multimodal Neurons in Pretrained Text-Only Transformers—0
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels—0
Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products—0
Multi-modal reward for visual relationships-based image captioning—0
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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