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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 451–500 of 1878 papers

TitleStatusHype
Jewelry Recognition via Encoder-Decoder Models—0
What Else Would I Like? A User Simulator using Alternatives for Improved Evaluation of Fashion Conversational Recommendation Systems—0
Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding—0
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning—0
Hyperparameter-Free Approach for Faster Minimum Bayes Risk DecodingCode0
Object-oriented backdoor attack against image captioning—0
SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment—0
Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only TrainingCode1
Social Media Ready Caption Generation for Brands—0
GPT-4V(ision) is a Generalist Web Agent, if GroundedCode4
Learning Vision from Models Rivals Learning Vision from DataCode2
TinyGPT-V: Efficient Multimodal Large Language Model via Small BackbonesCode3
Cycle-Consistency Learning for Captioning and Grounding—0
LLM4VG: Large Language Models Evaluation for Video Grounding—0
VCoder: Versatile Vision Encoders for Multimodal Large Language ModelsCode2
p-Laplacian Adaptation for Generative Pre-trained Vision-Language ModelsCode0
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language ModelsCode1
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart CaptioningCode1
VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and GenerationCode1
Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis—0
Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning—0
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense CaptionsCode1
Synocene, Beyond the Anthropocene: De-Anthropocentralising Human-Nature-AI Interaction—0
Filter & Align: Leveraging Human Knowledge to Curate Image-Text Data—0
Genixer: Empowering Multimodal Large Language Models as a Powerful Data GeneratorCode1
Unifying Text, Tables, and Images for Multimodal Question AnsweringCode0
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects—0
User-Aware Prefix-Tuning is a Good Learner for Personalized Image Captioning—0
PixLore: A Dataset-driven Approach to Rich Image CaptioningCode0
Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology VideosCode1
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
Mitigating Open-Vocabulary Caption HallucinationsCode1
Towards More Unified In-context Visual Understanding—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers and BERTCode0
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image CaptioningCode1
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human FeedbackCode6
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts—0
Video Summarization: Towards Entity-Aware CaptionsCode0
Enhancing Image Captioning with Neural Models—0
InstructSeq: Unifying Vision Tasks with Instruction-conditioned Multi-modal Sequence GenerationCode0
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images—0
Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language ModelsCode1
LLaMA-VID: An Image is Worth 2 Tokens in Large Language ModelsCode2
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training—0
EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension—0
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism—0
Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder—0
Improving Image Captioning via Predicting Structured Concepts—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