238 citations · 694 across the 33 of their papers we have counts for
18 papers · 1 filter
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings
Renjie Wei, Zechun Liu, Yuchen Fan +3
Deep neural networks for image super-resolution (SR) have demonstrated superior performance. However, the large memory and computation consumption hinders their deployment on resou…
Oscillation-free Quantization for Low-bit Vision Transformers
Shih-Yang Liu, Zechun Liu, Kwang-Ting Cheng
Weight oscillation is an undesirable side effect of quantization-aware training, in which quantized weights frequently jump between two quantized levels, resulting in training inst…
Stereo Neural Vernier Caliper
Shichao Li, Zechun Liu, Zhiqiang Shen +1
We propose a new object-centric framework for learning-based stereo 3D object detection. Previous studies build scene-centric representations that do not consider the significant v…
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space
Arnav Chavan, Zhiqiang Shen, Zhuang Liu +3
This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search…
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation
Zechun Liu, Kwang-Ting Cheng, Dong Huang +2
The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i.e., uniform strategy, due to its superior represent…
Sliced Recursive Transformer
Zhiqiang Shen, Zechun Liu, Eric Xing
We present a neat yet effective recursive operation on vision transformers that can improve parameter utilization without involving additional parameters. This is achieved by shari…
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning
Zhiqiang Shen, Zechun Liu, Jie Qin +2
The goal of few-shot learning is to learn a classifier that can recognize unseen classes from limited support data with labels. A common practice for this task is to train a model…
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration
Zhiqiang Shen, Zechun Liu, Jie Qin +3
Previous studies dominantly target at self-supervised learning on real-valued networks and have achieved many promising results. However, on the more challenging binary neural netw…
Conditional Link Prediction of Category-Implicit Keypoint Detection
Ellen Yi-Ge, Rui Fan, Zechun Liu +1
Keypoints of objects reflect their concise abstractions, while the corresponding connection links (CL) build the skeleton by detecting the intrinsic relations between keypoints. Ex…
Weight-dependent Gates for Network Pruning
Yun Li, Zechun Liu, Weiqun Wu +4
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. T…
Binarizing MobileNet via Evolution-based Searching
Hai Phan, Zechun Liu, Dang Huynh +3
Binary Neural Networks (BNNs), known to be one among the effectively compact network architectures, have achieved great outcomes in the visual tasks. Designing efficient binary arc…
Joint Multi-Dimension Pruning via Numerical Gradient Update
Zechun Liu, Xiangyu Zhang, Zhiqiang Shen +4
We present joint multi-dimension pruning (abbreviated as JointPruning), an effective method of pruning a network on three crucial aspects: spatial, depth and channel simultaneously…
Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
Zhiqiang Shen, Zechun Liu, Zhuang Liu +3
The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views…
ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions
Zechun Liu, Zhiqiang Shen, Marios Savvides +1
In this paper, we propose several ideas for enhancing a binary network to close its accuracy gap from real-valued networks without incurring any additional computational cost. We f…
Single Path One-Shot Neural Architecture Search with Uniform Sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu +4
We revisit the one-shot Neural Architecture Search (NAS) paradigm and analyze its advantages over existing NAS approaches. Existing one-shot method, however, is hard to train and n…
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
Zechun Liu, Haoyuan Mu, Xiangyu Zhang +4
In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is a…
Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance
Zechun Liu, Wenhan Luo, Baoyuan Wu +3
In this paper, we study 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While efficient, the lacking of representational capabilit…
Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm
Zechun Liu, Baoyuan Wu, Wenhan Luo +3
In this work, we study the 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While being efficient, the classification accuracy of t…