Stars
[CVPR-2024] Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
We introduce a novel approach for parameter generation, named neural network parameter diffusion (p-diff), which employs a standard latent diffusion model to synthesize a new set of parameters
[ICCV2023] Dataset Quantization
[ICCV 2023] MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
[CVPR-2023] Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation
[CVPR2023 Highlight] GRES: Generalized Referring Expression Segmentation
A benchmark dataset for GRES and GREC [CVPR2023 Highlight]
[TIP-2023] Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation
Efficient Dataset Distillation by Representative Matching
Lossless Training Speed Up by Unbiased Dynamic Data Pruning
[ICCV 2023] MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.
[ICCV2019] Boundary-Aware Feature Propagation for Scene Segmentation
A list of papers and datasets about point cloud analysis (processing)
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning
[ICCV2021 & TPAMI2023] Vision-Language Transformer and Query Generation for Referring Segmentation
[ICCV-2021] TransReID: Transformer-based Object Re-Identification
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
The official implementation of the CVPR2021 paper: Decoupled Dynamic Filter Networks
Official PyTorch implementation of SegFormer
Quasi-Dense Similarity Learning for Multiple Object Tracking, CVPR 2021 (Oral)
Resources for Multiple Object Tracking (MOT)
The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.
[ICCV'19] WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection
Open-source stronger baseline for unsupervised or domain adaptive object re-ID.
Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute