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 201–250 of 1878 papers

TitleStatusHype
Cross-Modal Consistency in Multimodal Large Language Models—0
Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted CaptionsCode0
Grounded Video Caption Generation—0
BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions—0
ViTOC: Vision Transformer and Object-aware Captioner—0
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models—0
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation ModelCode0
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
LLM2CLIP: Powerful Language Model Unlocks Richer Visual RepresentationCode4
RS-MoE: Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering—0
Designing a Robust Radiology Report Generation System—0
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP—0
Nearest Neighbor Normalization Improves Multimodal RetrievalCode1
Large Language Model Benchmarks in Medical Tasks—0
Image Generation from Image Captioning -- Invertible Approach—0
Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts—0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language TuningCode1
Altogether: Image Captioning via Re-aligning Alt-textCode0
Frontiers in Intelligent ColonoscopyCode2
VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use—0
TIPS: Text-Image Pretraining with Spatial AwarenessCode2
MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological imagesCode0
An Efficient System for Automatic Map Storytelling -- A Case Study on Historical MapsCode0
RAP: Retrieval-Augmented Personalization for Multimodal Large Language ModelsCode2
Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images—0
Self-adaptive Multimodal Retrieval-Augmented GenerationCode0
MMCFND: Multimodal Multilingual Caption-aware Fake News Detection for Low-resource Indic Languages—0
CLIP-SCGI: Synthesized Caption-Guided Inversion for Person Re-Identification—0
A Unified Debiasing Approach for Vision-Language Models across Modalities and TasksCode0
An Eye for an Ear: Zero-shot Audio Description Leveraging an Image Captioner using Audiovisual Distribution AlignmentCode0
Core Tokensets for Data-efficient Sequential Training of TransformersCode0
AnyAttack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models—0
CAPEEN: Image Captioning with Early Exits and Knowledge DistillationCode0
AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark—0
Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval—0
Backdooring Vision-Language Models with Out-Of-Distribution Data—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
Enhancing Explainability in Multimodal Large Language Models Using Ontological Context—0
A TextGCN-Based Decoding Approach for Improving Remote Sensing Image Captioning—0
IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot CaptioningCode1
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language ModelsCode4
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
YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language ModelsCode1
Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal ModelCode1
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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