Search Results for author: Rufeng Zhang

Found 9 papers, 7 papers with code

SOLO: A Simple Framework for Instance Segmentation

no code implementations30 Jun 2021 Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei LI

Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation.

Image Matting Instance Segmentation +4

TransTrack: Multiple Object Tracking with Transformer

2 code implementations31 Dec 2020 Peize Sun, Jinkun Cao, Yi Jiang, Rufeng Zhang, Enze Xie, Zehuan Yuan, Changhu Wang, Ping Luo

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems.

Ranked #6 on Multi-Object Tracking on SportsMOT (using extra training data)

Multi-Object Tracking Multiple Object Tracking with Transformer +3

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

6 code implementations CVPR 2021 Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei LI, Zehuan Yuan, Changhu Wang, Ping Luo

In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.

Object object-detection +2

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

6 code implementations CVPR 2021 Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei LI

Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin.

Contrastive Learning Image Classification +7

SOLOv2: Dynamic and Fast Instance Segmentation

18 code implementations NeurIPS 2020 Xinlong Wang, Rufeng Zhang, Tao Kong, Lei LI, Chunhua Shen

Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.

object-detection Object Detection +4

Cannot find the paper you are looking for? You can Submit a new open access paper.