345 citations · 2.2k across the 88 of their papers we have counts for
16 papers · 2 filters
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…
Heterogeneous Relational Complement for Vehicle Re-identification
Jiajian Zhao, Yifan Zhao, Jia Li +2
The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning vi…
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…
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
Guangyao Chen, Peixi Peng, Li Ma +3
Recently, the generalization behavior of Convolutional Neural Networks (CNN) is gradually transparent through explanation techniques with the frequency components decomposition. Ho…
An Information Theory-inspired Strategy for Automatic Network Pruning
Xiawu Zheng, Yuexiao Ma, Teng Xi +6
Despite superior performance on many computer vision tasks, deep convolution neural networks are well known to be compressed on devices that have resource constraints. Most existin…
VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
Xiao Wang, Jianing Li, Lin Zhu +6
Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lo…
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
Xiao Wang, Xiujun Shu, Shiliang Zhang +4
Many RGB-T trackers attempt to attain robust feature representation by utilizing an adaptive weighting scheme (or attention mechanism). Different from these works, we propose a new…
Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion
Mingbao Lin, Bohong Chen, Fei Chao +1
The mainstream approach for filter pruning is usually either to force a hard-coded importance estimation upon a computation-heavy pretrained model to select "important" filters, or…
Tracking by Joint Local and Global Search: A Target-aware Attention based Approach
Xiao Wang, Jin Tang, Bin Luo +3
Tracking-by-detection is a very popular framework for single object tracking which attempts to search the target object within a local search window for each frame. Although such l…
1xN Pattern for Pruning Convolutional Neural Networks
Mingbao Lin, Yuxin Zhang, Yuchao Li +6
Though network pruning receives popularity in reducing the complexity of convolutional neural networks (CNNs), it remains an open issue to concurrently maintain model accuracy as w…
Carrying out CNN Channel Pruning in a White Box
Yuxin Zhang, Mingbao Lin, Chia-Wen Lin +5
Channel Pruning has been long studied to compress CNNs, which significantly reduces the overall computation. Prior works implement channel pruning in an unexplainable manner, which…
Lottery Jackpots Exist in Pre-trained Models
Yuxin Zhang, Mingbao Lin, Yunshan Zhong +2
Network pruning is an effective approach to reduce network complexity with acceptable performance compromise. Existing studies achieve the sparsity of neural networks via time-cons…
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
Xiao Wang, Xiujun Shu, Zhipeng Zhang +4
Tracking by natural language specification is a new rising research topic that aims at locating the target object in the video sequence based on its language description. Compared…
Dynamic Attention guided Multi-Trajectory Analysis for Single Object Tracking
Xiao Wang, Zhe Chen, Jin Tang +4
Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions a…
Distilling a Powerful Student Model via Online Knowledge Distillation
Shaojie Li, Mingbao Lin, Yan Wang +4
Existing online knowledge distillation approaches either adopt the student with the best performance or construct an ensemble model for better holistic performance. However, the fo…
Adversarial Reciprocal Points Learning for Open Set Recognition
Guangyao Chen, Peixi Peng, Xiangqian Wang +1
Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning.The key chal…