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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–650 of 1878 papers

TitleStatusHype
Backdooring Vision-Language Models with Out-Of-Distribution Data—0
Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval—0
TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image CaptioningCode0
TrojVLM: Backdoor Attack Against Vision Language Models—0
DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning—0
A TextGCN-Based Decoding Approach for Improving Remote Sensing Image Captioning—0
Enhancing Explainability in Multimodal Large Language Models Using Ontological Context—0
Brotherhood at WMT 2024: Leveraging LLM-Generated Contextual Conversations for Cross-Lingual Image Captioning—0
Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption UtilizationCode0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs—0
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated ImagesCode0
Vision Language Models Can Parse Floor Plan Maps—0
LLMs Can Check Their Own Results to Mitigate Hallucinations in Traffic Understanding Tasks—0
Decoding Style: Efficient Fine-Tuning of LLMs for Image-Guided Outfit Recommendation with Preference—0
KALE: An Artwork Image Captioning System Augmented with Heterogeneous GraphCode0
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training—0
Evaluating authenticity and quality of image captions via sentiment and semantic analyses—0
Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings—0
BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding—0
Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial AttacksCode0
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding—0
PoseEmbroider: Towards a 3D, Visual, Semantic-aware Human Pose Representation—0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
Training-free Zero-shot Composed Image Retrieval via Weighted Modality Fusion and SimilarityCode0
FODA-PG for Enhanced Medical Imaging Narrative Generation: Adaptive Differentiation of Normal and Abnormal Attributes—0
No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning—0
Kvasir-VQA: A Text-Image Pair GI Tract DatasetCode0
Fluent and Accurate Image Captioning with a Self-Trained Reward Model—0
Hand1000: Generating Realistic Hands from Text with Only 1,000 Images—0
Pixels to Prose: Understanding the art of Image Captioning—0
Bidirectional Awareness Induction in Autoregressive Seq2Seq Models—0
Shifted Window Fourier Transform And Retention For Image Captioning—0
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks—0
PathInsight: Instruction Tuning of Multimodal Datasets and Models for Intelligence Assisted Diagnosis in Histopathology—0
Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy and Novel Ensemble Method—0
FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across TokenizersCode0
Enhancing Journalism with AI: A Study of Contextualized Image Captioning for News Articles using LLMs and LMMs—0
One Framework to Rule Them All: Unifying Multimodal Tasks with LLM Neural-Tuning—0
Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection—0
Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI—0
A Novel Evaluation Framework for Image2Text Generation—0
The Phantom Menace: Unmasking Privacy Leakages in Vision-Language Models—0
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning—0
VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks—0
SWIFT: Semantic Watermarking for Image Forgery ThwartingCode0
HICEScore: A Hierarchical Metric for Image Captioning EvaluationCode0
Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models—0
VideoGameBunny: Towards vision assistants for video games—0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Show:102550
← PrevPage 13 of 38Next →
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