270 citations · 920 across the 53 of their papers we have counts for
53 papers
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos
Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao +3
We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at th…
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
Zhiqiang Shen, Xiaoxiao Sheng, Hehe Fan +5
Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, a…
Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment
Sheng Zhang, Muzammal Naseer, Guangyi Chen +4
Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. Despite the success, most traditional VLMs-based methods are restricted by…
Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision
Xijie Huang, Zhiqiang Shen, Pingcheng Dong +1
Despite the outstanding performance of transformers in both language and vision tasks, the expanding computation and model size have increased the demand for efficient deployment.…
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
Zeyuan Yin, Eric Xing, Zhiqiang Shen
We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SReL) that decouples the bilevel optimization of model and synthetic data during training,…
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
Arnav Chavan, Zhuang Liu, Deepak Gupta +2
We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a generalized pro…
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning
Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao
We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely…
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos
Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang +3
Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost. In this…
Dropout Reduces Underfitting
Zhuang Liu, Zhiqiu Xu, Joseph Jin +2
Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for preventing overfitting in neural networks. In this study, we demonstrate that dropout c…
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery
Sheng Zhang, Salman Khan, Zhiqiang Shen +3
Although existing semi-supervised learning models achieve remarkable success in learning with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled…
i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?
Kevin Zhang, Zhiqiang Shen
Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned represe…
MixMask: Revisiting Masking Strategy for Siamese ConvNets
Kirill Vishniakov, Eric Xing, Zhiqiang Shen
The recent progress in self-supervised learning has successfully combined Masked Image Modeling (MIM) with Siamese Networks, harnessing the strengths of both methodologies. Nonethe…
SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li +5
In order to deploy deep models in a computationally efficient manner, model quantization approaches have been frequently used. In addition, as new hardware that supports mixed bitw…
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…
A Fast Knowledge Distillation Framework for Visual Recognition
Zhiqiang Shen, Eric Xing
While Knowledge Distillation (KD) has been recognized as a useful tool in many visual tasks, such as supervised classification and self-supervised representation learning, the main…
Data-Free Neural Architecture Search via Recursive Label Calibration
Zechun Liu, Zhiqiang Shen, Yun Long +3
This paper aims to explore the feasibility of neural architecture search (NAS) given only a pre-trained model without using any original training data. This is an important circums…
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types
Shentong Mo, Xi Fu, Chenyang Hong +6
In the genome biology research, regulatory genome modeling is an important topic for many regulatory downstream tasks, such as promoter classification, transaction factor binding s…
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…
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…
Unsupervised Disentanglement of Linear-Encoded Facial Semantics
Yutong Zheng, Yu-Kai Huang, Ran Tao +2
We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation l…