SOTAVerified

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 251–300 of 1878 papers

TitleStatusHype
PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language ModelCode1
Bi-LORA: A Vision-Language Approach for Synthetic Image DetectionCode1
RSGPT: A Remote Sensing Vision Language Model and BenchmarkCode1
FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report GenerationCode1
FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal ModelCode1
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion TasksCode1
Can We Talk Models Into Seeing the World Differently?Code1
Chart-to-Text: A Large-Scale Benchmark for Chart SummarizationCode1
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic RewardsCode1
Exploring Diverse In-Context Configurations for Image CaptioningCode1
Exchanging-based Multimodal Fusion with TransformerCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
CgT-GAN: CLIP-guided Text GAN for Image CaptioningCode1
CaMEL: Mean Teacher Learning for Image CaptioningCode1
ChatEarthNet: A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation ModelsCode1
Evolving Deep Neural NetworksCode1
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph ParsingCode1
From Association to Generation: Text-only Captioning by Unsupervised Cross-modal MappingCode1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense CaptionerCode1
EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression RecognitionCode1
Enhancing Visual Question Answering through Question-Driven Image Captions as PromptsCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
Dual-Level Collaborative Transformer for Image CaptioningCode1
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language GenerationCode1
Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation ModelsCode1
Discovering Autoregressive Orderings with Variational InferenceCode1
Discovering Non-monotonic Autoregressive Orderings with Variational InferenceCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart CaptioningCode1
Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image CaptioningCode1
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language ModelsCode1
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
End-to-End Supermask Pruning: Learning to Prune Image Captioning ModelsCode1
End-to-End Transformer Based Model for Image CaptioningCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
ACORT: A Compact Object Relation Transformer for Parameter Efficient Image CaptioningCode1
Can Audio Captions Be Evaluated with Image Caption Metrics?Code1
DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text RetrievalCode1
Are scene graphs good enough to improve Image Captioning?Code1
Exploiting Multiple Sequence Lengths in Fast End to End Training for Image CaptioningCode1
Exploring Discrete Diffusion Models for Image CaptioningCode1
BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual CuesCode1
Detecting and Recovering Sequential DeepFake ManipulationCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Detecting Hate Speech in Multi-modal MemesCode1
Brain Captioning: Decoding human brain activity into images and textCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
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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