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 51–100 of 1878 papers

TitleStatusHype
Shikra: Unleashing Multimodal LLM's Referential Dialogue MagicCode2
Scalable 3D Captioning with Pretrained ModelsCode2
Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math ReasoningCode2
Benchmarking and Improving Detail Image CaptionCode2
Controlling Length in Image CaptioningCode2
Comprehending and Ordering Semantics for Image CaptioningCode2
RAP: Retrieval-Augmented Personalization for Multimodal Large Language ModelsCode2
Yo'LLaVA: Your Personalized Language and Vision AssistantCode2
Unified Multimodal Discrete DiffusionCode2
Benchmarking Retrieval-Augmented Generation in Multi-Modal ContextsCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept MatchingCode2
Oscar: Object-Semantics Aligned Pre-training for Vision-Language TasksCode2
OmniCaptioner: One Captioner to Rule Them AllCode2
MeaCap: Memory-Augmented Zero-shot Image CaptioningCode2
OmniSearchSage: Multi-Task Multi-Entity Embeddings for Pinterest SearchCode2
PoseScript: Linking 3D Human Poses and Natural LanguageCode2
Text-Only Training for Image Captioning using Noise-Injected CLIPCode2
LLaMA-VID: An Image is Worth 2 Tokens in Large Language ModelsCode2
Keeping Yourself is Important in Downstream Tuning Multimodal Large Language ModelCode2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingCode2
Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory PredictionCode2
VHM: Versatile and Honest Vision Language Model for Remote Sensing Image AnalysisCode2
GLaMM: Pixel Grounding Large Multimodal ModelCode2
JourneyDB: A Benchmark for Generative Image UnderstandingCode2
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual DescriptionsCode2
Language Models Can See: Plugging Visual Controls in Text GenerationCode2
Learning Vision from Models Rivals Learning Vision from DataCode2
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language ModelsCode2
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual ContextsCode2
From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning TasksCode2
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language ModelsCode2
Frontiers in Intelligent ColonoscopyCode2
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIPCode2
Fine-grained Image Captioning with CLIP RewardCode2
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical TasksCode2
Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language ModelsCode2
ClipCap: CLIP Prefix for Image CaptioningCode2
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative InstructionsCode2
EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model EvaluationCode2
GIT: A Generative Image-to-text Transformer for Vision and LanguageCode2
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable FeaturesCode1
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal ModelCode1
Aesthetically Relevant Image CaptioningCode1
Bidimensional Leaderboards: Generate and Evaluate Language Hand in HandCode1
DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only TrainingCode1
Beyond Generation: Harnessing Text to Image Models for Object Detection and SegmentationCode1
Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image CaptioningCode1
Beyond Generic: Enhancing Image Captioning with Real-World Knowledge using Vision-Language Pre-Training ModelCode1
BERTGEN: Multi-task Generation through BERTCode1
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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