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 1051–1100 of 1878 papers

TitleStatusHype
Language Models are General-Purpose Interfaces—0
Intra-agent speech permits zero-shot task acquisition—0
Improving Image Captioning with Control Signal of Sentence Quality—0
Examining the Effects of Language-and-Vision Data Augmentation for Generation of Descriptions of Human Faces—0
Visual Transformer for Object Detection—0
BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions DatasetCode0
Variational Transformer: A Framework Beyond the Trade-off between Accuracy and Diversity for Image CaptioningCode0
Prompt-based Learning for Unpaired Image Captioning—0
Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset—0
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization—0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization—0
It Isn't Sh!tposting, It's My CAT Posting—0
Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures—0
Diverse Image Captioning with Grounded StyleCode0
Answer-Me: Multi-Task Open-Vocabulary Visual Question Answering—0
Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks—0
Combine to Describe: Evaluating Compositional Generalization in Image Captioning—0
Controllable Image Captioning—0
CapOnImage: Context-driven Dense-Captioning on Image—0
Cross-view Brain Decoding—0
Guiding Attention using Partial-Order Relationships for Image Captioning—0
Robust Cross-Modal Representation Learning with Progressive Self-Distillation—0
Semantic Exploration from Language Abstractions and Pretrained Representations—0
Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products—0
On Distinctive Image Captioning via Comparing and Reweighting—0
Learning Audio-Video Modalities from Image Captions—0
Socratic Models: Composing Zero-Shot Multimodal Reasoning with LanguageCode0
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models—0
AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation—0
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer—0
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Taking an Emotional Look at Video Paragraph Captioning—0
Geodesic Multi-Modal Mixup for Robust Fine-TuningCode0
Semantic Distillation Guided Salient Object Detection—0
Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition—0
A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanism—0
Interactive Machine Learning for Image Captioning—0
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning—0
Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigm—0
Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs—0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Deep Learning Approaches on Image Captioning: A Review—0
A Frustratingly Simple Approach for End-to-End Image Captioning—0
An Integrated Approach for Video Captioning and Applications—0
Visual Information Guided Zero-Shot Paraphrase GenerationCode0
Discovering Non-Monotonic Autoregressive Ordering for Text Generation Models using Sinkhorn Distributions—0
All You May Need for VQA are Image Captions—0
Long-Tail Classification for Distinctive Image Captioning: A Simple yet Effective Remedy for Side Effects of Reinforcement Learning—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