Papers with Code 아카이브
Image Captioning
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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)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LuoweiZhou/VLP
공식 코드Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/LuoweiZhou/e2e-gLSTM-sc
공식 코드Watch What You Just Said: Image Captioning with Text-Conditional Attention
https://github.com/Lx-Bao/QSFNet
공식 코드Quality-aware Selective Fusion Network for V-D-T Salient Object Detection
https://github.com/NataliaDiaz/Egoshots
공식 코드https://github.com/OSU-MLB/SSL-Foundation-Models
공식 코드Revisiting semi-supervised learning in the era of foundation models
https://github.com/OpenSpaceAI/Nesie
공식 코드Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection
https://github.com/Orion-AI-Lab/GAIA
공식 코드GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image Analysis
https://github.com/PaddlePaddle/PaddleDetection
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/arsl
공식 코드Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det/semi_detr
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_generation/unimo-text
공식 코드UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
https://github.com/PaloAltoNetworks/CCP_CIFAR
공식 코드Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
https://github.com/Panda-Peter/image-captioning
공식 코드https://github.com/PengyiZhang/ACCL
공식 코드ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation
https://github.com/PixArt-alpha/PixArt-sigma
공식 코드PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
https://github.com/Pranav21091996/Semantic_Fidelity-and-Egoshots
공식 코드https://github.com/PrasannaPulakurthi/SPM
공식 코드https://github.com/Qi2019KB/UBPL-PoseEstimation
공식 코드https://github.com/QingtaoPan/DuSSS
공식 코드https://github.com/RS-CSU/MemoryAdaptNet-master
공식 코드https://github.com/RiTUAL-UH/multimodal_NER
공식 코드Can images help recognize entities? A study of the role of images for Multimodal NER
https://github.com/RitaRamo/retrieval-augmentation-nn
공식 코드https://github.com/RotsteinNoam/FuseCap
공식 코드FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions
https://github.com/RoyalSkye/ATCL
공식 코드Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
https://github.com/RuijieZhu94/EC-Depth
공식 코드https://github.com/SALT-NLP/LLaVAR
공식 코드LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
https://github.com/SHI-Labs/Semi-Supervised-Transfer-Learning
공식 코드Adaptive Consistency Regularization for Semi-Supervised Transfer Learning
https://github.com/SY-Xuan/IICS
공식 코드Intra-Inter Camera Similarity for Unsupervised Person Re-Identification
https://github.com/SadilKhan/MARVEL-FX3D
공식 코드MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content Creation
https://github.com/SamsungLabs/CIC-BART-SSA
공식 코드CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation
https://github.com/SeleenaJM/CapEval
공식 코드https://github.com/ShannonAI/Neural-Semi-Supervised-Learning-for-Text-Classification
공식 코드Neural Semi-supervised Learning for Text Classification Under Large-Scale Pretraining
https://github.com/Smith42/pix2prof
공식 코드https://github.com/Soldelli/MAD
공식 코드https://github.com/SunJiamei/LRP-imagecaptioning-pytorch
공식 코드Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.