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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 101–150 of 1878 papers

TitleStatusHype
ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting—0
What Is a Good Caption? A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness—0
InsightVision: A Comprehensive, Multi-Level Chinese-based Benchmark for Evaluating Implicit Visual Semantics in Large Vision Language Models—0
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models—0
Pretrained Image-Text Models are Secretly Video CaptionersCode0
GroundCap: A Visually Grounded Image Captioning Dataset—0
TPCap: Unlocking Zero-Shot Image Captioning with Trigger-Augmented and Multi-Modal Purification Modules—0
VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models—0
GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image AnalysisCode1
FE-LWS: Refined Image-Text Representations via Decoder Stacking and Fused Encodings for Remote Sensing Image Captioning—0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
Evaluation of Multilingual Image Captioning: How far can we get with CLIP models?Code0
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—0
Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal UnderstandingCode3
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents—0
Efficient Few-Shot Continual Learning in Vision-Language Models—0
TexLiDAR: Automated Text Understanding for Panoramic LiDAR DataCode0
Exploring Spatial Language Grounding Through Referring Expressions—0
COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation—0
Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language ModelsCode1
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding—0
Large Vision-Language Models for Knowledge-Grounded Data Annotation of MemesCode0
An Ensemble Model with Attention Based Mechanism for Image Captioning—0
PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language ModelCode1
Text-driven Adaptation of Foundation Models for Few-shot Surgical Workflow AnalysisCode0
LAVCap: LLM-based Audio-Visual Captioning using Optimal TransportCode1
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness—0
VCRScore: Image captioning metric based on V\&L Transformers, CLIP, and precision-recall—0
RadAlign: Advancing Radiology Report Generation with Vision-Language Concept AlignmentCode1
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
Valley2: Exploring Multimodal Models with Scalable Vision-Language DesignCode3
Improving Image Captioning by Mimicking Human Reformulation Feedback at Inference-time—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?Code1
Decoding fMRI Data into Captions using Prefix Language ModelingCode0
MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning—0
Patch Matters: Training-free Fine-grained Image Caption Enhancement via Local Perception—0
AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation—0
Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image CaptioningCode1
Semantic and Expressive Variations in Image Captions Across Languages—0
Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
ErgoChat: a Visual Query System for the Ergonomic Risk Assessment of Construction Workers—0
ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image CaptioningCode0
EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model EvaluationCode2
Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy—0
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object LocalizationCode0
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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