226 citations · 1.6k across the 138 of their papers we have counts for
7 papers · 2 filters
Disentangled High Quality Salient Object Detection
Lv Tang, Bo Li, Shouhong Ding +1
Aiming at discovering and locating most distinctive objects from visual scenes, salient object detection (SOD) plays an essential role in various computer vision systems. Coming to…
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision
Bo Li, Xinyang Jiang, Donglin Bai +6
The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of…
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis
Xianjing Liu, Bo Li, Esther Bron +3
Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent t…
FILTRA: Rethinking Steerable CNN by Filter Transform
Bo Li, Qili Wang, Gim Hee Lee
Steerable CNN imposes the prior knowledge of transformation invariance or equivariance in the network architecture to enhance the the network robustness on geometry transformation…
Self-Supervised Pretraining Improves Self-Supervised Pretraining
Colorado J. Reed, Xiangyu Yue, Ani Nrusimha +9
While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to da…
Real-Time Visual Object Tracking via Few-Shot Learning
Jinghao Zhou, Bo Li, Peng Wang +5
Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL). While the concept of FSL is not new in tracking and has been previously applied by prior wo…
Higher Performance Visual Tracking with Dual-Modal Localization
Jinghao Zhou, Bo Li, Lei Qiao +5
Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. While most existing works fail to operate simultaneously on both, we investigate in this work t…