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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 151–200 of 1878 papers

TitleStatusHype
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction GenerationCode0
Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-EvolutionCode0
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage—0
Reframing Image Difference Captioning with BLIP2IDC and Synthetic AugmentationCode0
Dataset Augmentation by Mixing Visual Concepts—0
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language ModelsCode0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Descriptive Caption Enhancement with Visual Specialists for Multimodal PerceptionCode0
G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4oCode1
JoVALE: Detecting Human Actions in Video Using Audiovisual and Language ContextsCode0
Typhoon 2: A Family of Open Text and Multimodal Thai Large Language ModelsCode1
Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval—0
MedMax: Mixed-Modal Instruction Tuning for Training Biomedical AssistantsCode1
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension—0
UnMA-CapSumT: Unified and Multi-Head Attention-driven Caption Summarization Transformer—0
Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track—0
From Simple to Professional: A Combinatorial Controllable Image Captioning AgentCode0
Optimizing Vision-Language Interactions Through Decoder-Only Models—0
Automated Image Captioning with CNNs and TransformersCode0
Vision-Language Models Represent Darker-Skinned Black Individuals as More Homogeneous than Lighter-Skinned Black Individuals—0
Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image CaptioningCode1
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models—0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation—0
Exploring Multi-Grained Concept Annotations for Multimodal Large Language ModelsCode0
HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing—0
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep LearningCode0
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth FusionCode3
Personalizing Multimodal Large Language Models for Image Captioning: An Experimental Analysis—0
Remote Sensing Temporal Vision-Language Models: A Comprehensive SurveyCode3
Progress-Aware Video Frame Captioning—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding—0
Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring—0
Sparse Attention Vectors: Generative Multimodal Model Features Are Discriminative Vision-Language Classifiers—0
OPCap:Object-aware Prompting Captioning—0
Active Data Curation Effectively Distills Large-Scale Multimodal Models—0
Efficient Multi-modal Large Language Models via Visual Token Grouping—0
LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report GenerationCode1
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models—0
Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks—0
FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity—0
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report GenerationCode1
Uterine Ultrasound Image Captioning Using Deep Learning Techniques—0
LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image CompressionCode1
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment—0
AI Flow at the Network Edge—0
The Power of Many: Multi-Agent Multimodal Models for Cultural Image CaptioningCode0
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-TuningCode0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—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