26 citations · 60 across the 7 of their papers we have counts for
14 papers
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…
How Do Adam and Training Strategies Help BNNs Optimization?
Zechun Liu, Zhiqiang Shen, Shichao Li +3
The best performing Binary Neural Networks (BNNs) are usually attained using Adam optimization and its multi-step training variants. However, to the best of our knowledge, few stud…
"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization
Tianlong Chen, Zhenyu Zhang, Xu Ouyang +3
Batch normalization (BN) is a key facilitator and considered essential for state-of-the-art binary neural networks (BNN). However, the BN layer is costly to calculate and is typica…
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
Zhiqiang Shen, Zechun Liu, Dejia Xu +3
This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation. We begin by introducing the motivation beh…
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…