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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gkioxari/RstarCNN
공식 코드https://github.com/gkw0010/gtav_head-dataset
공식 코드https://github.com/gladzhang/art
공식 코드Accurate Image Restoration with Attention Retractable Transformer
https://github.com/goddesshs/transmrsr
공식 코드TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution
https://github.com/gohtanii/DiverSeg-dataset
공식 코드Rethinking Image Super-Resolution from Training Data Perspectives
https://github.com/gokulkarthik/deformable-detr
공식 코드An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
https://github.com/gokulkarthik/detr
공식 코드An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
https://github.com/gokyildirim/salmon_dataset
공식 코드https://github.com/goodarzmehr/simbev
공식 코드SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset
https://github.com/goodproj13/SG-Net
공식 코드SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation
https://github.com/goodproj13/tf-blender
공식 코드TF-Blender: Temporal Feature Blender for Video Object Detection
https://github.com/google-deepmind/long-form-factuality
공식 코드https://github.com/google-research-datasets/Objectron
공식 코드Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations
https://github.com/google-research/augmix
공식 코드AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/google-research/big_vision
공식 코드How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/google-research/deeplab2
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/fvlm
공식 코드F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models
https://github.com/google-research/google-research/tree/master/fvlm/dito
공식 코드Region-centric Image-Language Pretraining for Open-Vocabulary Detection
https://github.com/google-research/google-research/tree/master/fvlm/rovit
공식 코드Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers
https://github.com/google-research/google-research/tree/master/muller
공식 코드https://github.com/google-research/google-research/tree/master/persistent-nature
공식 코드Persistent Nature: A Generative Model of Unbounded 3D Worlds
https://github.com/google-research/lag
공식 코드Creating High Resolution Images with a Latent Adversarial Generator
https://github.com/google-research/long-range-arena
공식 코드https://github.com/google-research/maxim
공식 코드https://github.com/google-research/maxvit
공식 코드https://github.com/google-research/pegasus
공식 코드Investigating Efficiently Extending Transformers for Long Input Summarization
https://github.com/google-research/pix2seq
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit
공식 코드Simple Open-Vocabulary Object Detection with Vision Transformers
https://github.com/google-research/ssl_detection
공식 코드A Simple Semi-Supervised Learning Framework for Object Detection
https://github.com/google-research/vision_transformer
공식 코드How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/google/automl
공식 코드https://github.com/gorilla-lab-scut/vista
공식 코드VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention
https://github.com/gpitsilis/hate-speech
공식 코드https://github.com/gpt4vision/OvSGTR
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.