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 701–750 of 1878 papers

TitleStatusHype
Im2Text: Describing Images Using 1 Million Captioned Photographs—0
Florenz: Scaling Laws for Systematic Generalization in Vision-Language Models—0
Flowing from Words to Pixels: A Framework for Cross-Modality Evolution—0
Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution—0
A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation—0
Fluent and Accurate Image Captioning with a Self-Trained Reward Model—0
Focused Evaluation for Image Description with Binary Forced-Choice Tasks—0
Focus! Relevant and Sufficient Context Selection for News Image Captioning—0
FODA-PG for Enhanced Medical Imaging Narrative Generation: Adaptive Differentiation of Normal and Abnormal Attributes—0
Foundation Models for Remote Sensing: An Analysis of MLLMs for Object Localization—0
Attend More Times for Image Captioning—0
FaceGemma: Enhancing Image Captioning with Facial Attributes for Portrait Images—0
Extended Self-Critical Pipeline for Transforming Videos to Text (TRECVID-VTT Task 2021) -- Team: MMCUniAugsburg—0
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models—0
Comparative study of Transformer and LSTM Network with attention mechanism on Image Captioning—0
How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey—0
From Pixels to Prose: A Large Dataset of Dense Image Captions—0
Aligning Large Multimodal Models with Factually Augmented RLHF—0
From Show to Tell: A Survey on Deep Learning-based Image Captioning—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model—0
Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models—0
FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs—0
Exploring Visual Relationship for Image Captioning—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Fusion Models for Improved Visual Captioning—0
A Survey of Evaluation Metrics Used for NLG Systems—0
How Vision Affects Language: Comparing Masked Self-Attention in Uni-Modal and Multi-Modal Transformer—0
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model—0
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
Exploring Visual Culture Awareness in GPT-4V: A Comprehensive Probing—0
AstroLLaVA: towards the unification of astronomical data and natural language—0
Generalized Visual Relation Detection with Diffusion Models—0
3D Spatial Understanding in MLLMs: Disambiguation and Evaluation—0
Exploring Affordance and Situated Meaning in Image Captions: A Multimodal Analysis—0
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models—0
Generating Description for Sequential Images with Local-Object Attention Conditioned on Global Semantic Context—0
Generating Diverse and Descriptive Image Captions Using Visual Paraphrases—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Generating Diverse and Informative Natural Language Fashion Feedback—0
HOW IMPORTANT ARE NETWORK WEIGHTS? TO WHAT EXTENT DO THEY NEED AN UPDATE?—0
Generating image captions with external encyclopedic knowledge—0
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions—0
Attention Strategies for Multi-Source Sequence-to-Sequence Learning—0
Exploring Spatial Language Grounding Through Referring Expressions—0
Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity—0
Generating Triples with Adversarial Networks for Scene Graph Construction—0
Generating Video Descriptions with Topic Guidance—0
Connecting Language and Vision to Actions—0
Exploring Semantic Relationships for Unpaired Image Captioning—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