https://github.com/microsoft/unilm/tree/master/beit
공식 코드Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
Papers with Code 아카이브
2D Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/vision-longformer
공식 코드Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
https://github.com/miguelag99/efficient-instance-prediction
공식 코드Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction
https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets
공식 코드Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
https://github.com/miilab-iitgn/xai4bci
공식 코드https://github.com/mikasa3lili/ssd-monodetr
공식 코드SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection
https://github.com/mikasenghaas/sillystill
공식 코드https://github.com/mikuhatsune/wsod_transfer
공식 코드Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
https://github.com/mile888/anomaly_identification
공식 코드https://github.com/milesigel/audio-sentiment-transfer
공식 코드https://github.com/mileyan/Pseudo_Lidar_V2
공식 코드Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
https://github.com/mileyan/pseudo-LiDAR_e2e
공식 코드https://github.com/mileyan/pseudo_lidar
공식 코드https://github.com/mililab/roma
공식 코드RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
https://github.com/milinpatel07/SOTIF-PCOD
공식 코드https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege
공식 코드Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge
https://github.com/mindgarage-shan/trans_object_detection_survey
공식 코드https://github.com/mindgarage-shan/transformer_object_detection_survey
공식 코드https://github.com/mindslab-ai/assem-vc
공식 코드Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques
https://github.com/mindslab-ai/cotatron
공식 코드Cotatron: Transcription-Guided Speech Encoder for Any-to-Many Voice Conversion without Parallel Data
https://github.com/mindspore-ai/models/tree/master/research/cv/ISyNet
공식 코드ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/mindspore-ai/models/tree/master/research/cv/TokenFusion
공식 코드https://github.com/mindspore-lab/mindediting
공식 코드Tunable Convolutions with Parametric Multi-Loss Optimization
https://github.com/mindspore-lab/models
공식 코드https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/DECO
공식 코드https://github.com/ming024/FastSpeech2
공식 코드https://github.com/ming71/CFC-Net
공식 코드https://github.com/ming71/DAL
공식 코드Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
https://github.com/ming71/RIDet
공식 코드Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
https://github.com/mingbohong/yola
공식 코드You Only Look Around: Learning Illumination Invariant Feature for Low-light Object Detection
https://github.com/mingjiechen/dyganvc
공식 코드Efficient Non-Autoregressive GAN Voice Conversion using VQWav2vec Features and Dynamic Convolution
https://github.com/mingkaid/rl-prompt
공식 코드RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
https://github.com/mingsun-tse/collaborative-distillation
공식 코드Collaborative Distillation for Ultra-Resolution Universal Style Transfer
https://github.com/mingzi151/interpretationdata
공식 코드It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
https://github.com/minliu01/non-adversarial_backdoor
공식 코드https://github.com/mintelligence-group/mmfeed
공식 코드Affective Feedback Synthesis Towards Multimodal Text and Image Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.