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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
FuseCap: Leveraging Large Language Models for Enriched Fused Image CaptionsCode1
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene GraphCode1
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
Fooling Contrastive Language-Image Pre-trained Models with CLIPMasterPrintsCode1
CLIPScore: A Reference-free Evaluation Metric for Image CaptioningCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine TranslationCode1
CIDEr: Consensus-based Image Description EvaluationCode1
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in ContextCode1
Chart-to-Text: A Large-Scale Benchmark for Chart SummarizationCode1
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic RewardsCode1
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report GenerationCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph ParsingCode1
A Survey on Efficient Vision-Language ModelsCode1
Can Audio Captions Be Evaluated with Image Caption Metrics?Code1
Exploiting Multiple Sequence Lengths in Fast End to End Training for Image CaptioningCode1
Evolving Deep Neural NetworksCode1
Adapting Grad-CAM for Embedding NetworksCode1
Exchanging-based Multimodal Fusion with TransformerCode1
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation ModelsCode1
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion TasksCode1
CgT-GAN: CLIP-guided Text GAN for Image CaptioningCode1
Exploring Discrete Diffusion Models for Image CaptioningCode1
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language GenerationCode1
CaMEL: Mean Teacher Learning for Image CaptioningCode1
FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal ModelCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual CuesCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Evaluating Multimodal Representations on Visual Semantic Textual SimilarityCode1
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree SearchCode1
Coarse-to-Fine Vision-Language Pre-training with Fusion in the BackboneCode1
GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image AnalysisCode1
CNN+CNN: Convolutional Decoders for Image CaptioningCode1
Exploring Diverse In-Context Configurations for Image CaptioningCode1
PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language ModelCode1
COCO-Stuff: Thing and Stuff Classes in ContextCode1
CoCa: Contrastive Captioners are Image-Text Foundation ModelsCode1
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
A large annotated corpus for learning natural language inferenceCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
GRIT: Faster and Better Image captioning Transformer Using Dual Visual FeaturesCode1
AutoAD: Movie Description in ContextCode1
Comprehensive Image Captioning via Scene Graph DecompositionCode1
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual ConceptsCode1
Confidence-aware Non-repetitive Multimodal Transformers for TextCapsCode1
Analog Bits: Generating Discrete Data using Diffusion Models with Self-ConditioningCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
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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