3 code implementations • CVPR 2023 • Jiacheng Zhang, Xiangru Lin, Wei zhang, Kuo Wang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li
Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
1 code implementation • CVPR 2023 • Chang Liu, Weiming Zhang, Xiangru Lin, Wei zhang, Xiao Tan, Junyu Han, Xiaomao Li, Errui Ding, Jingdong Wang
It employs a "divide-and-conquer" strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity.
Ranked #1 on Semi-Supervised Object Detection on COCO 10% labeled data (detector metric)
1 code implementation • 8 Mar 2024 • Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, Zeyu Wang
We present SplattingAvatar, a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh, which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device.
1 code implementation • CVPR 2019 • Weifeng Ge, Xiangru Lin, Yizhou Yu
We build complementary parts models in a weakly supervised manner to retrieve information suppressed by dominant object parts detected by convolutional neural networks.
Ranked #21 on Fine-Grained Image Classification on CUB-200-2011
1 code implementation • ICCV 2023 • Jiaming Li, Xiangru Lin, Wei zhang, Xiao Tan, YingYing Li, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
no code implementations • CVPR 2021 • Xiangru Lin, Guanbin Li, Yizhou Yu
Intuitively, we comprehend the semantics of the instruction to form an overview of where a bathroom is and what a blue towel is in mind; then, we navigate to the target location by consistently matching the bathroom appearance in mind with the current scene.
no code implementations • CVPR 2021 • Haoyu Ma, Xiangru Lin, Zifeng Wu, Yizhou Yu
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
Ranked #23 on Synthetic-to-Real Translation on SYNTHIA-to-Cityscapes
no code implementations • 3 Jan 2023 • Haoyu Ma, Xiangru Lin, Yizhou Yu
This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation.
no code implementations • 26 Mar 2024 • Jiacheng Zhang, Jiaming Li, Xiangru Lin, Wei zhang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.