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 1251–1300 of 1878 papers

TitleStatusHype
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
Beam-Guided Knowledge Replay for Knowledge-Rich Image Captioning using Vision-Language Model—0
@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology—0
Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigm—0
Better Captioning with Sequence-Level Exploration—0
Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring—0
Better Understanding Hierarchical Visual Relationship for Image Caption—0
Beyond Caption To Narrative: Video Captioning With Multiple Sentences—0
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States—0
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—0
BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation—0
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand—0
Bidirectional Awareness Induction in Autoregressive Seq2Seq Models—0
Bidirectional Beam Search: Forward-Backward Inference in Neural Sequence Models for Fill-in-the-Blank Image Captioning—0
Improving Medical Visual Representations via Radiology Report Generation—0
Evaluating Text-to-Image Matching using Binary Image Selection (BISON)—0
BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding—0
BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions—0
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks—0
Boosted Attention: Leveraging Human Attention for Image Captioning—0
Boost Image Captioning with Knowledge Reasoning—0
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations—0
Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph—0
Boosting Image Captioning with Attributes—0
Bornon: Bengali Image Captioning with Transformer-based Deep learning approach—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
Bridge the Gap: High-level Semantic Planning for Image Captioning—0
Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings—0
Bringing back simplicity and lightliness into neural image captioning—0
Brotherhood at WMT 2024: Leveraging LLM-Generated Contextual Conversations for Cross-Lingual Image Captioning—0
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning—0
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing—0
Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks—0
CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization—0
Can Neural Image Captioning be Controlled via Forced Attention?—0
Can Prompt Learning Benefit Radiology Report Generation?—0
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design—0
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era—0
CapOnImage: Context-driven Dense-Captioning on Image—0
CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection—0
CAPTION: Correction by Analyses, POS-Tagging and Interpretation of Objects using only Nouns—0
Captioning Images Taken by People Who Are Blind—0
Captioning Images with Novel Objects via Online Vocabulary Expansion—0
Captions Are Worth a Thousand Words: Enhancing Product Retrieval with Pretrained Image-to-Text Models—0
CapWAP: Captioning with a Purpose—0
CapWAP: Image Captioning with a Purpose—0
Categorizing and Inferring the Relationship between the Text and Image of Twitter Posts—0
Categorizing Concepts With Basic Level for Vision-to-Language—0
Causal reasoning in typical computer vision tasks—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—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