427 citations · 1.1k across the 82 of their papers we have counts for
11 papers · 1 filter
Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue
Shuwei Shao, Zhongcai Pei, Weihai Chen +4
Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scena…
POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing
Sheng Xu, Yanjing Li, Junhe Zhao +2
Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address…
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
Yunshan Zhong, Mingbao Lin, Gongrui Nan +4
Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of the r…
Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One
Shuwei Shao, Ran Li, Zhongcai Pei +5
Depth estimation attracts widespread attention in the computer vision community. However, it is still quite difficult to recover an accurate depth map using only one RGB image. We…
Prioritized Subnet Sampling for Resource-Adaptive Supernet Training
Bohong Chen, Mingbao Lin, Rongrong Ji +1
A resource-adaptive supernet adjusts its subnets for inference to fit the dynamically available resources. In this paper, we propose prioritized subnet sampling to train a resource…
Cogradient Descent for Dependable Learning
Runqi Wang, Baochang Zhang, Li'an Zhuo +2
Conventional gradient descent methods compute the gradients for multiple variables through the partial derivative. Treating the coupled variables independently while ignoring the i…
Oriented Object Detection with Transformer
Teli Ma, Mingyuan Mao, Honghui Zheng +6
Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN. However, the potential of DETR remains largely un…
Probabilistic Ranking-Aware Ensembles for Enhanced Object Detections
Mingyuan Mao, Baochang Zhang, David Doermann +5
Model ensembles are becoming one of the most effective approaches for improving object detection performance already optimized for a single detector. Conventional methods directly…
Interpretable Attention Guided Network for Fine-grained Visual Classification
Zhenhuan Huang, Xiaoyue Duan, Bo Zhao +2
Fine-grained visual classification (FGVC) is challenging but more critical than traditional classification tasks. It requires distinguishing different subcategories with the inhere…
SiMaN: Sign-to-Magnitude Network Binarization
Mingbao Lin, Rongrong Ji, Zihan Xu +4
Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in…
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning
Huan Chang, Yicheng Chen, Baochang Zhang +1
Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers. However…