lq0920

lq0920's Stars

• amusi/CVPR2024-Papers-with-Code

  CVPR 2024 论文和开源项目合集

  17.7k2882022.6k

• KaiyangZhou/CoOp

  Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)

  Language:Python1.7k1578191

• ucbdrive/few-shot-object-detection

  Implementations of few-shot object detection benchmarks

  Language:Python1.1k28186223

• houqb/CoordAttention

  Code for our CVPR2021 paper coordinate attention

  Language:Python998949121

• Zeleni9/pytorch-wgan

  Pytorch implementation of DCGAN, WGAN-CP, WGAN-GP

  Language:Python720529144

• caoyunkang/Segment-Any-Anomaly

  Official implementation of "Segment Any Anomaly without Training via Hybrid Prompt Regularization (SAA+)".

  Language:Jupyter Notebook711103275

• open-mmlab/mmfewshot

  OpenMMLab FewShot Learning Toolbox and Benchmark

  Language:Python6931195116

• muzairkhattak/multimodal-prompt-learning

  [CVPR 2023] Official repository of paper titled "MaPLe: Multi-modal Prompt Learning".

  Language:Python63068146

• gaopengcuhk/Tip-Adapter

  Language:Python52962740

• bingykang/Fewshot_Detection

  Few-shot Object Detection via Feature Reweighting

  Language:Python5281690111

• zhenyuw16/UniDetector

  Code release for our CVPR 2023 paper "Detecting Everything in the Open World: Towards Universal Object Detection".

  Language:Python526143524

• Ixiaohuihuihui/Tiny-Defect-Detection-for-PCB

  This is a repository about PCB defect detection.

  Language:Jupyter Notebook4251455148

• zhengye1995/Tianchi-2019-Guangdong-Intelligent-identification-of-cloth-defects-rank5

  天池2019广东工业智造创新大赛 布匹疵点检测 天池水也太深了 季军解决方案

  Language:Python398733142

• fanq15/Few-Shot-Object-Detection-Dataset

  379213058

• megvii-research/FSCE

  Language:Python279117347

• fanq15/FSOD-code

  Language:Python24496762

• cizhenshi/TianchiGuangdong2019_2th

  Language:Python21842864

• zae-bayern/elpv-dataset

  A dataset of functional and defective solar cells extracted from EL images of solar modules

  Language:Python20714574

• zbwxp/SegVit

  Official Pytorch Implementation of SegViT: Semantic Segmentation with Plain Vision Transformers

  Language:Python20622120

• ZiqinZhou66/ZegCLIP

  Official implement of CVPR2023 ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation

  Language:Python196112517

• ByChelsea/VAND-APRIL-GAN

  [CVPR 2023 Workshop] VAND Challenge: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

  Language:Python19233521

• MingiJi/FRSKD

  Official implementation for (Refine Myself by Teaching Myself : Feature Refinement via Self-Knowledge Distillation, CVPR-2021)

  Language:Python922925

• GuangxingHan/FCT

  Code for CVPR 2022 Oral paper: 'Few-Shot Object Detection with Fully Cross-Transformer'

  Language:Python7243013

• Ravby/eswa-template

  ESWA - Expert Systems with Applications latex template

  Language:TeX69156

• binyisu/PVEL-AD

  Photovoltaic cell defect detection

  Language:Python68447

• yzfly/CVPR2023_Top_Open_Papers

  This repository is a curated collection of the most exciting and influential CVPR 2023 opensource works [Paper + Code].🔥

  Language:HTML59305

• Ze-Yang/Efficient-FSOD

  The official implementation of Efficient Few-Shot Object Detection via Knowledge Inheritance (TIP 2022)

  Language:Python30414

• gaobb/DCFS

  [NeurIPS 2022] Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

  Language:Python29182

• RSer-XDU/G-FSDet

  Language:Python230176

• nanxiaotong/STKD

  Knowledge distillation (KD) transfers discriminative knowledge from a large and complex model (known as Teacher) to a smaller and faster one (known as Student). Existing advanced knowledge distillation methods, limited to fixed feature extraction paradigms that capture teacher’s structure knowledge to guide the training of the student, often fail to obtain comprehensive knowledge to the student. Toward this end, in this paper, we propose a new approach, Synchronous Teaching Knowledge Distillation (STKD), to integrate online teaching and offline teaching for transferring rich and comprehensive knowledge to the student.

  Language:Python611