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 1501–1550 of 1878 papers

TitleStatusHype
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models—0
Exploring Affordance and Situated Meaning in Image Captions: A Multimodal Analysis—0
Exploring Visual Culture Awareness in GPT-4V: A Comprehensive Probing—0
Exploring Visual Relationship for Image Captioning—0
Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models—0
Extended Self-Critical Pipeline for Transforming Videos to Text (TRECVID-VTT Task 2021) -- Team: MMCUniAugsburg—0
FaceGemma: Enhancing Image Captioning with Facial Attributes for Portrait Images—0
"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention—0
``Factual'' or ``Emotional'': Stylized Image Captioning with Adaptive Learning and Attention—0
FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning—0
FAIEr: Fidelity and Adequacy Ensured Image Caption Evaluation—0
Fairness in AI Systems: Mitigating gender bias from language-vision models—0
Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech—0
Fast Image Caption Generation with Position Alignment—0
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity—0
Feature Difference Makes Sense: A medical image captioning model exploiting feature difference and tag information—0
Feature Fusion Effects of Tensor Product Representation on (De)Compositional Network for Caption Generation for Images—0
Feedback is Needed for Retakes: An Explainable Poor Image Notification Framework for the Visually Impaired—0
FE-LWS: Refined Image-Text Representations via Decoder Stacking and Fused Encodings for Remote Sensing Image Captioning—0
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches—0
Figure Captioning with Reasoning and Sequence-Level Training—0
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting—0
FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity—0
Fine-Grained Video Captioning through Scene Graph Consolidation—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—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
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
From “Before” to “After”: Generating Natural Language Instructions from Image Pairs in a Simple Visual Domain—0
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models—0
From Pixels to Prose: A Large Dataset of Dense Image Captions—0
From Show to Tell: A Survey on Deep Learning-based Image Captioning—0
FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs—0
Fusion Models for Improved Visual Captioning—0
Gaussian Smoothen Semantic Features (GSSF) -- Exploring the Linguistic Aspects of Visual Captioning in Indian Languages (Bengali) Using MSCOCO Framework—0
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
Generalized Visual Relation Detection with Diffusion Models—0
Generalizing Image Captions for Image-Text Parallel Corpus—0
Generating captions without looking beyond objects—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
Generating Diverse and Informative Natural Language Fashion Feedback—0
Generating Image Captions in Arabic using Root-Word Based Recurrent Neural Networks and Deep Neural Networks—0
Generating image captions with external encyclopedic knowledge—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