b'Zhiqiang Shen'

research

∙ 05/22/2023

Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning

We present a new self-supervised paradigm on point cloud sequence unders...

0 Xiaoxiao Sheng, et al. ∙

research

∙ 05/06/2023

PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos

Self-supervised learning can extract representations of good quality fro...

0 Zhiqiang Shen, et al. ∙

research

∙ 03/02/2023

Dropout Reduces Underfitting

Introduced by Hinton et al. in 2012, dropout has stood the test of time ...

0 Zhuang Liu, et al. ∙

research

∙ 01/11/2023

Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

High-quality pseudo labels are essential for semi-supervised semantic se...

0 Zhiqiang Shen, et al. ∙

research

∙ 12/11/2022

PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery

Although existing semi-supervised learning models achieve remarkable suc...

0 Sheng Zhang, et al. ∙

research

∙ 10/20/2022

i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

Masked image modeling (MIM) has been recognized as a strong and popular ...

0 Kevin Zhang, et al. ∙

research

∙ 10/20/2022

MixMask: Revisiting Masked Siamese Self-supervised Learning in Asymmetric Distance

Recent advances in self-supervised learning integrate Masked Modeling an...

0 Kirill Vishniakov, et al. ∙

research

∙ 01/03/2022

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

This paper explores the feasibility of finding an optimal sub-model from...

17 Arnav Chavan, et al. ∙

research

∙ 12/03/2021

Data-Free Neural Architecture Search via Recursive Label Calibration

This paper aims to explore the feasibility of neural architecture search...

1 Zechun Liu, et al. ∙

research

∙ 12/02/2021

A Fast Knowledge Distillation Framework for Visual Recognition

While Knowledge Distillation (KD) has been recognized as a useful tool i...

33 Zhiqiang Shen, et al. ∙

research

∙ 11/29/2021

Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation

The nonuniform quantization strategy for compressing neural networks usu...

11 Zechun Liu, et al. ∙

research

∙ 11/09/2021

Sliced Recursive Transformer

We present a neat yet effective recursive operation on vision transforme...

7 Zhiqiang Shen, et al. ∙

research

∙ 10/11/2021

Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types

In the genome biology research, regulatory genome modeling is an importa...

15 Shentong Mo, et al. ∙

research

∙ 06/21/2021

How Do Adam and Training Strategies Help BNNs Optimization?

The best performing Binary Neural Networks (BNNs) are usually attained u...

1 Zechun Liu, et al. ∙

research

∙ 05/23/2021

COTR: Convolution in Transformer Network for End to End Polyp Detection

Purpose: Colorectal cancer (CRC) is the second most common cause of canc...

0 Zhiqiang Shen, et al. ∙

research

∙ 04/16/2021

"BNN - BN = ?": Training Binary Neural Networks without Batch Normalization

Batch normalization (BN) is a key facilitator and considered essential f...

0 Tianlong Chen, et al. ∙

research

∙ 04/01/2021

Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study

This work aims to empirically clarify a recently discovered perspective ...

0 Zhiqiang Shen, et al. ∙

research

∙ 03/30/2021

Unsupervised Disentanglement of Linear-Encoded Facial Semantics

We propose a method to disentangle linear-encoded facial semantics from ...

0 Yutong Zheng, et al. ∙

research

∙ 02/22/2021

Interpretative Computer-aided Lung Cancer Diagnosis: from Radiology Analysis to Malignancy Evaluation

Background and Objective:Computer-aided diagnosis (CAD) systems promote ...

18 Shaohua Zheng, et al. ∙

research

∙ 02/17/2021

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration

Previous studies dominantly target at self-supervised learning on real-v...

0 Zhiqiang Shen, et al. ∙

research

∙ 02/08/2021

Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning

The goal of few-shot learning is to learn a classifier that can recogniz...

0 Zhiqiang Shen, et al. ∙

research

∙ 11/29/2020

Conditional Link Prediction of Category-Implicit Keypoint Detection

Keypoints of objects reflect their concise abstractions, while the corre...

0 Ellen Yi-Ge, et al. ∙

research

∙ 11/15/2020

Online Ensemble Model Compression using Knowledge Distillation

This paper presents a novel knowledge distillation based model compressi...

0 Devesh Walawalkar, et al. ∙

research

∙ 09/17/2020

MEAL V2: Boosting Vanilla ResNet-50 to 80 without Tricks

In this paper, we introduce a simple yet effective approach that can boo...

0 Zhiqiang Shen, et al. ∙

research

∙ 09/07/2020

Channel-wise Alignment for Adaptive Object Detection

Generic object detection has been immensely promoted by the development ...

0 Hang Yang, et al. ∙

research

∙ 06/26/2020

Cross-Ssupervised Object Detection

After learning a new object category from image-level annotations (with ...

17 Zitian Chen, et al. ∙

research

∙ 06/26/2020

Cross-Supervised Object Detection

After learning a new object category from image-level annotations (with ...

6 Zitian Chen, et al. ∙

research

∙ 05/18/2020

Joint Multi-Dimension Pruning

We present joint multi-dimension pruning (named as JointPruning), a new ...

6 Zechun Liu, et al. ∙

research

∙ 05/13/2020

Binarizing MobileNet via Evolution-based Searching

Binary Neural Networks (BNNs), known to be one among the effectively com...

0 Hai Phan, et al. ∙

research

∙ 03/29/2020

Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification

Convolutional neural networks (CNN) are capable of learning robust repre...

0 Devesh Walawalkar, et al. ∙

research

∙ 03/11/2020

Rethinking Image Mixture for Unsupervised Visual Representation Learning

In supervised learning, smoothing label/prediction distribution in neura...

8 Zhiqiang Shen, et al. ∙

research

∙ 03/07/2020

ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions

In this paper, we propose several ideas for enhancing a binary network t...

0 Zechun Liu, et al. ∙

research

∙ 02/12/2020

Solving Missing-Annotation Object Detection with Background Recalibration Loss

This paper focuses on a novel and challenging detection scenario: A majo...

0 Han Zhang, et al. ∙

research

∙ 11/27/2019

Soft Anchor-Point Object Detection

Recently, anchor-free detectors have shown great potential to outperform...

0 Chenchen Zhu, et al. ∙

research

∙ 11/06/2019

SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses

Unsupervised domain adaptive object detection aims to learn a robust det...

24 Zhiqiang Shen, et al. ∙

research

∙ 10/21/2019

Transferable Recognition-Aware Image Processing

Recent progress in image recognition has stimulated the deployment of vi...

0 Zhuang Liu, et al. ∙

research

∙ 08/22/2019

Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and Noisy Data Refinement

Generic Image recognition is a fundamental and fairly important visual p...

24 Zhiqiang Shen, et al. ∙

research

∙ 07/29/2019

MoBiNet: A Mobile Binary Network for Image Classification

MobileNet and Binary Neural Networks are two among the most widely used ...

2 Hai Phan, et al. ∙

research

∙ 05/05/2019

Towards Instance-level Image-to-Image Translation

Unpaired Image-to-image Translation is a new rising and challenging visi...

8 Zhiqiang Shen, et al. ∙

research

∙ 02/25/2019

Transfer Learning for Sequences via Learning to Collocate

Transfer learning aims to solve the data sparsity for a target domain by...

0 Wanyun Cui, et al. ∙

research

∙ 12/06/2018

MEAL: Multi-Model Ensemble via Adversarial Learning

Often the best performing deep neural models are ensembles of multiple b...

0 Zhiqiang Shen, et al. ∙

research

∙ 09/25/2018

Object Detection from Scratch with Deep Supervision

We propose Deeply Supervised Object Detectors (DSOD), an object detectio...

4 Zhiqiang Shen, et al. ∙

research

∙ 07/13/2018

TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection

This work provides a simple approach to discover tight object bounding b...

10 Yunchao Wei, et al. ∙

research

∙ 12/04/2017

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

In this paper, we propose gated recurrent feature pyramid for the proble...

0 Zhiqiang Shen, et al. ∙

research

∙ 08/22/2017

Learning Efficient Convolutional Networks through Network Slimming

The deployment of deep convolutional neural networks (CNNs) in many real...

0 Zhuang Liu, et al. ∙

research

∙ 08/03/2017

DSOD: Learning Deeply Supervised Object Detectors from Scratch

We present Deeply Supervised Object Detector (DSOD), a framework that ca...

0 Zhiqiang Shen, et al. ∙

research

∙ 04/05/2017

Weakly Supervised Dense Video Captioning

This paper focuses on a novel and challenging vision task, dense video c...

0 Zhiqiang Shen, et al. ∙

research

∙ 03/29/2017

Iterative Object and Part Transfer for Fine-Grained Recognition

The aim of fine-grained recognition is to identify sub-ordinate categori...

0 Zhiqiang Shen, et al. ∙

research

∙ 09/24/2014

Do More Dropouts in Pool5 Feature Maps for Better Object Detection

Deep Convolutional Neural Networks (CNNs) have gained great success in i...

0 Zhiqiang Shen, et al. ∙

Zhiqiang Shen

Featured Co-authors