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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 501–550 of 1878 papers

TitleStatusHype
Evaluation of Multilingual Image Captioning: How far can we get with CLIP models?Code0
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—0
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
COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation—0
Exploring Spatial Language Grounding Through Referring Expressions—0
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
Text-driven Adaptation of Foundation Models for Few-shot Surgical Workflow AnalysisCode0
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
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
Improving Image Captioning by Mimicking Human Reformulation Feedback at Inference-time—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
Decoding fMRI Data into Captions using Prefix Language ModelingCode0
MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning—0
Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models—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
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
Semantic and Expressive Variations in Image Captions Across Languages—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
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy—0
SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object LocalizationCode0
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
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction GenerationCode0
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
Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval—0
JoVALE: Detecting Human Actions in Video Using Audiovisual and Language ContextsCode0
UnMA-CapSumT: Unified and Multi-Head Attention-driven Caption Summarization Transformer—0
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension—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
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models—0
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation—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