Search Results for author: Baochang Zhang

Found 96 papers, 34 papers with code

Heterogeneous Generative Knowledge Distillation with Masked Image Modeling

no code implementations18 Sep 2023 ZiMing Wang, Shumin Han, Xiaodi Wang, Jing Hao, Xianbin Cao, Baochang Zhang

Masked image modeling (MIM) methods achieve great success in various visual tasks but remain largely unexplored in knowledge distillation for heterogeneous deep models.

Image Classification Knowledge Distillation +3

Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler

1 code implementation1 Jul 2023 Shaohui Lin, Wenxuan Huang, Jiao Xie, Baochang Zhang, Yunhang Shen, Zhou Yu, Jungong Han, David Doermann

In this paper, we propose a novel Knowledge-driven Differential Filter Sampler~(KDFS) with Masked Filter Modeling~(MFM) framework for filter pruning, which globally prunes the redundant filters based on the prior knowledge of a pre-trained model in a differential and non-alternative optimization.

Image Classification Network Pruning

DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs

no code implementations27 Jun 2023 Yanjing Li, Sheng Xu, Xianbin Cao, Li'an Zhuo, Baochang Zhang, Tian Wang, Guodong Guo

One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a unified framework, while searching the 1-bit CNNs is more challenging due to the more complicated processes involved.

Neural Architecture Search object-detection +2

Decom--CAM: Tell Me What You See, In Details! Feature-Level Interpretation via Decomposition Class Activation Map

no code implementations27 May 2023 Yuguang Yang, Runtang Guo, Sheng Wu, Yimi Wang, Juan Zhang, Xuan Gong, Baochang Zhang

Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting object location, it fails to provide insight into the salient features used by the model to make decisions.

Decision Making

Bi-ViT: Pushing the Limit of Vision Transformer Quantization

no code implementations21 May 2023 Yanjing Li, Sheng Xu, Mingbao Lin, Xianbin Cao, Chuanjian Liu, Xiao Sun, Baochang Zhang

Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices.

Binarization Quantization

AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning

no code implementations CVPR 2023 Runqi Wang, Xiaoyue Duan, Guoliang Kang, Jianzhuang Liu, Shaohui Lin, Songcen Xu, Jinhu Lv, Baochang Zhang

Text consists of a category name and a fixed number of learnable parameters which are selected from our designed attribute word bank and serve as attributes.

Continual Learning Language Modelling

Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution Alignment

1 code implementation CVPR 2023 Runqi Wang, Hao Zheng, Xiaoyue Duan, Jianzhuang Liu, Yuning Lu, Tian Wang, Songcen Xu, Baochang Zhang

However, with only a few training images, there exist two crucial problems: (1) the visual feature distributions are easily distracted by class-irrelevant information in images, and (2) the alignment between the visual and language feature distributions is difficult.

Few-Shot Learning

Controllable Mind Visual Diffusion Model

no code implementations17 May 2023 Bohan Zeng, Shanglin Li, Xuhui Liu, Sicheng Gao, XiaoLong Jiang, Xu Tang, Yao Hu, Jianzhuang Liu, Baochang Zhang

Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models.

Image Generation

MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation

no code implementations14 Apr 2023 Jie Guo, Qimeng Wang, Yan Gao, XiaoLong Jiang, Xu Tang, Yao Hu, Baochang Zhang

CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations.

GPR Open Vocabulary Semantic Segmentation +2

Q-DETR: An Efficient Low-Bit Quantized Detection Transformer

1 code implementation CVPR 2023 Sheng Xu, Yanjing Li, Mingbao Lin, Peng Gao, Guodong Guo, Jinhu Lu, Baochang Zhang

At the upper level, we introduce a new foreground-aware query matching scheme to effectively transfer the teacher information to distillation-desired features to minimize the conditional information entropy.

object-detection Object Detection +1

Implicit Diffusion Models for Continuous Super-Resolution

1 code implementation CVPR 2023 Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, XianTong Zhen, Baochang Zhang

IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation.

Denoising Image Super-Resolution

Resilient Binary Neural Network

1 code implementation2 Feb 2023 Sheng Xu, Yanjing Li, Teli Ma, Mingbao Lin, Hao Dong, Baochang Zhang, Peng Gao, Jinhu Lv

In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training.

Feature Calibration Network for Occluded Pedestrian Detection

no code implementations12 Dec 2022 Tianliang Zhang, Qixiang Ye, Baochang Zhang, Jianzhuang Liu, Xiaopeng Zhang, Qi Tian

FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module.

Pedestrian Detection

CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection

no code implementations12 Dec 2022 Tianliang Zhang, Zhenjun Han, Huijuan Xu, Baochang Zhang, Qixiang Ye

In this paper we propose a novel feature learning model, referred to as CircleNet, to achieve feature adaptation by mimicking the process humans looking at low resolution and occluded objects: focusing on it again, at a finer scale, if the object can not be identified clearly for the first time.

object-detection Object Detection +1

Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning

no code implementations28 Nov 2022 Xiaoyue Duan, Guoliang Kang, Runqi Wang, Shumin Han, Song Xue, Tian Wang, Baochang Zhang

Based on this observation, we propose a simple strategy, i. e., increasing the number of training shots, to mitigate the loss of intrinsic dimension caused by robustness-promoting regularization.


Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

1 code implementation13 Oct 2022 Yanjing Li, Sheng Xu, Baochang Zhang, Xianbin Cao, Peng Gao, Guodong Guo

The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices.


IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

1 code implementation7 Oct 2022 Sheng Xu, Yanjing Li, Bohan Zeng, Teli Ma, Baochang Zhang, Xianbin Cao, Peng Gao, Jinhu Lv

This explains why existing KD methods are less effective for 1-bit detectors, caused by a significant information discrepancy between the real-valued teacher and the 1-bit student.

Knowledge Distillation object-detection +1

FNeVR: Neural Volume Rendering for Face Animation

1 code implementation21 Sep 2022 Bohan Zeng, Boyu Liu, Hong Li, Xuhui Liu, Jianzhuang Liu, Dapeng Chen, Wei Peng, Baochang Zhang

In FNeVR, we design a 3D Face Volume Rendering (FVR) module to enhance the facial details for image rendering.

Talking Face Generation

Recurrent Bilinear Optimization for Binary Neural Networks

2 code implementations4 Sep 2022 Sheng Xu, Yanjing Li, Tiancheng Wang, Teli Ma, Baochang Zhang, Peng Gao, Yu Qiao, Jinhu Lv, Guodong Guo

To address this issue, Recurrent Bilinear Optimization is proposed to improve the learning process of BNNs (RBONNs) by associating the intrinsic bilinear variables in the back propagation process.

object-detection Object Detection

MAFormer: A Transformer Network with Multi-scale Attention Fusion for Visual Recognition

no code implementations31 Aug 2022 Yunhao Wang, Huixin Sun, Xiaodi Wang, Bin Zhang, Chao Li, Ying Xin, Baochang Zhang, Errui Ding, Shumin Han

We develop a simple but effective module to explore the full potential of transformers for visual representation by learning fine-grained and coarse-grained features at a token level and dynamically fusing them.

Instance Segmentation object-detection +2

Anti-Retroactive Interference for Lifelong Learning

1 code implementation27 Aug 2022 Runqi Wang, Yuxiang Bao, Baochang Zhang, Jianzhuang Liu, Wentao Zhu, Guodong Guo

Second, according to the similarity between incremental knowledge and base knowledge, we design an adaptive fusion of incremental knowledge, which helps the model allocate capacity to the knowledge of different difficulties.


Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework

1 code implementation8 May 2022 Chunyu Xie, Jincheng Li, Heng Cai, Fanjing Kong, Xiaoyu Wu, Jianfei Song, Henrique Morimitsu, Lin Yao, Dexin Wang, Dawei Leng, Baochang Zhang, Xiangyang Ji, Yafeng Deng

Along with the ZERO benchmark, we also develop a VLP framework with pre-Ranking + Ranking mechanism, boosted with target-guided Distillation and feature-guided Distillation (R2D2) for large-scale cross-modal learning.

Image Classification Image Retrieval +6

Bi-level Doubly Variational Learning for Energy-based Latent Variable Models

no code implementations CVPR 2022 Ge Kan, Jinhu Lü, Tian Wang, Baochang Zhang, Aichun Zhu, Lei Huang, Guodong Guo, Hichem Snoussi

In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs.

Image Generation Image Reconstruction +1

TerViT: An Efficient Ternary Vision Transformer

no code implementations20 Jan 2022 Sheng Xu, Yanjing Li, Teli Ma, Bohan Zeng, Baochang Zhang, Peng Gao, Jinhu Lv

Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices.

Associative Adversarial Learning Based on Selective Attack

no code implementations28 Dec 2021 Runqi Wang, Xiaoyue Duan, Baochang Zhang, Song Xue, Wentao Zhu, David Doermann, Guodong Guo

We show that our method improves the recognition accuracy of adversarial training on ImageNet by 8. 32% compared with the baseline.

Adversarial Robustness Few-Shot Learning +2

Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue

1 code implementation15 Dec 2021 Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu, Dianmin Sun, Baochang Zhang

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios.

Depth Estimation Motion Estimation +1

POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing

no code implementations26 Nov 2021 Sheng Xu, Yanjing Li, Junhe Zhao, Baochang Zhang, Guodong Guo

Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices.

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization

1 code implementation CVPR 2022 Yunshan Zhong, Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, Rongrong Ji

In this paper, we observe an interesting phenomenon of intra-class heterogeneity in real data and show that existing methods fail to retain this property in their synthetic images, which causes a limited performance increase.


Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than One

no code implementations16 Nov 2021 Shuwei Shao, Ran Li, Zhongcai Pei, Zhong Liu, Weihai Chen, Wentao Zhu, Xingming Wu, Baochang Zhang

In this work, we investigate into the phenomenon and propose to integrate the strengths of multiple weak depth predictor to build a comprehensive and accurate depth predictor, which is critical for many real-world applications, e. g., 3D reconstruction.

3D Reconstruction Ensemble Learning +2

Cogradient Descent for Dependable Learning

no code implementations20 Jun 2021 Runqi Wang, Baochang Zhang, Li'an Zhuo, Qixiang Ye, David Doermann

Conventional gradient descent methods compute the gradients for multiple variables through the partial derivative.

Image Inpainting Image Reconstruction +1

Layer-Wise Searching for 1-Bit Detectors

no code implementations CVPR 2021 Sheng Xu, Junhe Zhao, Jinhu Lu, Baochang Zhang, Shumin Han, David Doermann

At each layer, it exploits a differentiable binarization search (DBS) to minimize the angular error in a student-teacher framework.


Oriented Object Detection with Transformer

no code implementations6 Jun 2021 Teli Ma, Mingyuan Mao, Honghui Zheng, Peng Gao, Xiaodi Wang, Shumin Han, Errui Ding, Baochang Zhang, David Doermann

Object detection with Transformers (DETR) has achieved a competitive performance over traditional detectors, such as Faster R-CNN.

object-detection Object Detection +1

Dual-stream Network for Visual Recognition

no code implementations NeurIPS 2021 Mingyuan Mao, Renrui Zhang, Honghui Zheng, Peng Gao, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han

Transformers with remarkable global representation capacities achieve competitive results for visual tasks, but fail to consider high-level local pattern information in input images.

Image Classification Instance Segmentation +3

SiMaN: Sign-to-Magnitude Network Binarization

2 code implementations16 Feb 2021 Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Fei Chao, Chia-Wen Lin, Ling Shao

In this paper, we show that our weight binarization provides an analytical solution by encoding high-magnitude weights into +1s, and 0s otherwise.


Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning

no code implementations3 Feb 2021 Huan Chang, Yicheng Chen, Baochang Zhang, David Doermann

Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers.

Edge-computing reinforcement-learning +1

IDARTS: Interactive Differentiable Architecture Search

no code implementations ICCV 2021 Song Xue, Runqi Wang, Baochang Zhang, Tian Wang, Guodong Guo, David Doermann

Differentiable Architecture Search (DARTS) improves the efficiency of architecture search by learning the architecture and network parameters end-to-end.

Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models

1 code implementation ICCV 2021 Jie Li, Rongrong Ji, Peixian Chen, Baochang Zhang, Xiaopeng Hong, Ruixin Zhang, Shaoxin Li, Jilin Li, Feiyue Huang, Yongjian Wu

A common practice is to start from a large perturbation and then iteratively reduce it with a deterministic direction and a random one while keeping it adversarial.

Dimensionality Reduction

Fast Class-wise Updating for Online Hashing

no code implementations1 Dec 2020 Mingbao Lin, Rongrong Ji, Xiaoshuai Sun, Baochang Zhang, Feiyue Huang, Yonghong Tian, DaCheng Tao

To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches.

A Review of Recent Advances of Binary Neural Networks for Edge Computing

no code implementations24 Nov 2020 Wenyu Zhao, Teli Ma, Xuan Gong, Baochang Zhang, David Doermann

Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection.

Edge-computing Neural Architecture Search +3

PAMS: Quantized Super-Resolution via Parameterized Max Scale

1 code implementation ECCV 2020 Huixia Li, Chenqian Yan, Shaohui Lin, Xiawu Zheng, Yuchao Li, Baochang Zhang, Fan Yang, Rongrong Ji

Specifically, most state-of-the-art SR models without batch normalization have a large dynamic quantization range, which also serves as another cause of performance drop.

Quantization Super-Resolution +1

Rotated Binary Neural Network

2 code implementations NeurIPS 2020 Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin

In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.

Binarization Quantization

The 1st Tiny Object Detection Challenge:Methods and Results

1 code implementation16 Sep 2020 Xuehui Yu, Zhenjun Han, Yuqi Gong, Nan Jiang, Jian Zhao, Qixiang Ye, Jie Chen, Yuan Feng, Bin Zhang, Xiaodi Wang, Ying Xin, Jingwei Liu, Mingyuan Mao, Sheng Xu, Baochang Zhang, Shumin Han, Cheng Gao, Wei Tang, Lizuo Jin, Mingbo Hong, Yuchao Yang, Shuiwang Li, Huan Luo, Qijun Zhao, Humphrey Shi

The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.

Human Detection object-detection +1

Binarized Neural Architecture Search for Efficient Object Recognition

no code implementations8 Sep 2020 Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, Rongrong Ji, David Doermann, Guodong Guo

In this paper, binarized neural architecture search (BNAS), with a search space of binarized convolutions, is introduced to produce extremely compressed models to reduce huge computational cost on embedded devices for edge computing.

Edge-computing Face Recognition +2

Anti-Bandit Neural Architecture Search for Model Defense

1 code implementation ECCV 2020 Hanlin Chen, Baochang Zhang, Song Xue, Xuan Gong, Hong Liu, Rongrong Ji, David Doermann

Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks.

Denoising Neural Architecture Search

iffDetector: Inference-aware Feature Filtering for Object Detection

1 code implementation23 Jun 2020 Mingyuan Mao, Yuxin Tian, Baochang Zhang, Qixiang Ye, Wanquan Liu, Guodong Guo, David Doermann

In this paper, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages.

object-detection Object Detection

Cogradient Descent for Bilinear Optimization

no code implementations CVPR 2020 Li'an Zhuo, Baochang Zhang, Linlin Yang, Hanlin Chen, Qixiang Ye, David Doermann, Guodong Guo, Rongrong Ji

Conventional learning methods simplify the bilinear model by regarding two intrinsically coupled factors independently, which degrades the optimization procedure.

Image Reconstruction Network Pruning

CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks

no code implementations30 Apr 2020 Li'an Zhuo, Baochang Zhang, Hanlin Chen, Linlin Yang, Chen Chen, Yanjun Zhu, David Doermann

To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).

Neural Architecture Search

NAS-Count: Counting-by-Density with Neural Architecture Search

no code implementations ECCV 2020 Yutao Hu, Xiao-Long Jiang, Xuhui Liu, Baochang Zhang, Jungong Han, Xian-Bin Cao, David Doermann

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts.

Crowd Counting Density Estimation +1

HRank: Filter Pruning using High-Rank Feature Map

2 code implementations CVPR 2020 Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, Ling Shao

The principle behind our pruning is that low-rank feature maps contain less information, and thus pruned results can be easily reproduced.

Network Pruning Vocal Bursts Intensity Prediction

Channel Pruning via Automatic Structure Search

1 code implementation23 Jan 2020 Mingbao Lin, Rongrong Ji, Yuxin Zhang, Baochang Zhang, Yongjian Wu, Yonghong Tian

In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i. e., channel number in each layer, rather than selecting "important" channels as previous works did.

Variational Structured Semantic Inference for Diverse Image Captioning

no code implementations NeurIPS 2019 Fuhai Chen, Rongrong Ji, Jiayi Ji, Xiaoshuai Sun, Baochang Zhang, Xuri Ge, Yongjian Wu, Feiyue Huang, Yan Wang

To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema.

Image Captioning

GBCNs: Genetic Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

no code implementations25 Nov 2019 Chunlei Liu, Wenrui Ding, Yuan Hu, Baochang Zhang, Jianzhuang Liu, Guodong Guo

The BGA method is proposed to modify the binary process of GBCNs to alleviate the local minima problem, which can significantly improve the performance of 1-bit DCNNs.

Face Recognition Object Recognition +1

Binarized Neural Architecture Search

1 code implementation25 Nov 2019 Hanlin Chen, Li'an Zhuo, Baochang Zhang, Xiawu Zheng, Jianzhuang Liu, David Doermann, Rongrong Ji

A variant, binarized neural architecture search (BNAS), with a search space of binarized convolutions, can produce extremely compressed models.

Neural Architecture Search

Circulant Binary Convolutional Networks: Enhancing the Performance of 1-bit DCNNs with Circulant Back Propagation

no code implementations CVPR 2019 Chunlei Liu, Wenrui Ding, Xin Xia, Baochang Zhang, Jiaxin Gu, Jianzhuang Liu, Rongrong Ji, David Doermann

The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs).

Aggregation Signature for Small Object Tracking

no code implementations24 Oct 2019 Chunlei Liu, Wenrui Ding, Jinyu Yang, Vittorio Murino, Baochang Zhang, Jungong Han, Guodong Guo

In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift.

Object Tracking

Semantic-aware Image Deblurring

no code implementations9 Oct 2019 Fuhai Chen, Rongrong Ji, Chengpeng Dai, Xiaoshuai Sun, Chia-Wen Lin, Jiayi Ji, Baochang Zhang, Feiyue Huang, Liujuan Cao

Specially, we propose a novel Structured-Spatial Semantic Embedding model for image deblurring (termed S3E-Deblur), which introduces a novel Structured-Spatial Semantic tree model (S3-tree) to bridge two basic tasks in computer vision: image deblurring (ImD) and image captioning (ImC).

Deblurring Image Captioning +1

RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

no code implementations21 Aug 2019 Chunlei Liu, Wenrui Ding, Xin Xia, Yuan Hu, Baochang Zhang, Jianzhuang Liu, Bohan Zhuang, Guodong Guo

Binarized convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications.

Binarization Object Tracking

Bayesian Optimized 1-Bit CNNs

no code implementations ICCV 2019 Jiaxin Gu, Junhe Zhao, Xiao-Long Jiang, Baochang Zhang, Jianzhuang Liu, Guodong Guo, Rongrong Ji

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models.

Interpretable Neural Network Decoupling

no code implementations ECCV 2020 Yuchao Li, Rongrong Ji, Shaohui Lin, Baochang Zhang, Chenqian Yan, Yongjian Wu, Feiyue Huang, Ling Shao

More specifically, we introduce a novel architecture controlling module in each layer to encode the network architecture by a vector.

Network Interpretation

Supervised Online Hashing via Similarity Distribution Learning

no code implementations31 May 2019 Mingbao Lin, Rongrong Ji, Shen Chen, Feng Zheng, Xiaoshuai Sun, Baochang Zhang, Liujuan Cao, Guodong Guo, Feiyue Huang

In this paper, we propose to model the similarity distributions between the input data and the hashing codes, upon which a novel supervised online hashing method, dubbed as Similarity Distribution based Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship in the produced Hamming space.


DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning

1 code implementation28 May 2019 Xiawu Zheng, Chenyi Yang, Shaokun Zhang, Yan Wang, Baochang Zhang, Yongjian Wu, Yunsheng Wu, Ling Shao, Rongrong Ji

With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints, which is practical for on-device models across diverse search spaces and constraints.

Neural Architecture Search

Multinomial Distribution Learning for Effective Neural Architecture Search

1 code implementation ICCV 2019 Xiawu Zheng, Rongrong Ji, Lang Tang, Baochang Zhang, Jianzhuang Liu, Qi Tian

Therefore, NAS can be transformed to a multinomial distribution learning problem, i. e., the distribution is optimized to have a high expectation of the performance.

Neural Architecture Search

Towards Optimal Structured CNN Pruning via Generative Adversarial Learning

1 code implementation CVPR 2019 Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, David Doermann

In this paper, we propose an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner.

Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network

no code implementations3 Mar 2019 Xiaolong Jiang, Zehao Xiao, Baochang Zhang, Xian-Tong Zhen, Xian-Bin Cao, David Doermann, Ling Shao

In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps.

Crowd Counting Density Estimation

Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression

1 code implementation CVPR 2019 Yuchao Li, Shaohui Lin, Baochang Zhang, Jianzhuang Liu, David Doermann, Yongjian Wu, Feiyue Huang, Rongrong Ji

The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression.

Clustering Model Compression

Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation

no code implementations30 Nov 2018 Jiaxin Gu, Ce Li, Baochang Zhang, Jungong Han, Xian-Bin Cao, Jianzhuang Liu, David Doermann

The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy of recognition systems for many computer vision tasks.

Object detection and tracking benchmark in industry based on improved correlation filter

no code implementations11 Jun 2018 Shangzhen Luan, Yan Li, Xiaodi Wang, Baochang Zhang

Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4. 0 applications.

object-detection Real-Time Object Detection

Modulated Convolutional Networks

no code implementations CVPR 2018 Xiaodi Wang, Baochang Zhang, Ce Li, Rongrong Ji, Jungong Han, Xian-Bin Cao, Jianzhuang Liu

In this paper, we propose new Modulated Convolutional Networks (MCNs) to improve the portability of CNNs via binarized filters.

Memory Attention Networks for Skeleton-based Action Recognition

1 code implementation23 Apr 2018 Chunyu Xie, Ce Li, Baochang Zhang, Chen Chen, Jungong Han, Changqing Zou, Jianzhuang Liu

Specifically, the TARM is deployed in a residual learning module that employs a novel attention learning network to recalibrate the temporal attention of frames in a skeleton sequence.

Action Recognition Skeleton Based Action Recognition +1

The Structure Transfer Machine Theory and Applications

1 code implementation1 Apr 2018 Baochang Zhang, Lian Zhuo, Ze Wang, Jungong Han, Xian-Tong Zhen

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown.

Image Classification Object Tracking +1

Latent Constrained Correlation Filter

no code implementations11 Nov 2017 Baochang Zhang, Shangzhen Luan, Chen Chen, Jungong Han, Wei Wang, Alessandro Perina, Ling Shao

In this paper, we introduce an intermediate step -- solution sampling -- after the data sampling step to form a subspace, in which an optimal solution can be estimated.

Object Recognition Object Tracking

Manifold Constrained Low-Rank Decomposition

no code implementations6 Aug 2017 Chen Chen, Baochang Zhang, Alessio Del Bue, Vittorio Murino

Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling.

Cross-Modality Binary Code Learning via Fusion Similarity Hashing

no code implementations CVPR 2017 Hong Liu, Rongrong Ji, Yongjian Wu, Feiyue Huang, Baochang Zhang

In this paper, we propose a hashing scheme, termed Fusion Similarity Hashing (FSH), which explicitly embeds the graph-based fusion similarity across modalities into a common Hamming space.


Gabor Convolutional Networks

no code implementations3 May 2017 Shangzhen Luan, Baochang Zhang, Chen Chen, Xian-Bin Cao, Jungong Han, Jianzhuang Liu

Steerable properties dominate the design of traditional filters, e. g., Gabor filters, and endow features the capability of dealing with spatial transformations.

Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model

no code implementations CVPR 2017 Qixiang Ye, Tianliang Zhang, Qiang Qiu, Baochang Zhang, Jie Chen, Guillermo Sapiro

In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved.

Object Discovery Object Localization +4

Latent Constrained Correlation Filters for Object Localization

no code implementations7 Jun 2016 Shangzhen Luan, Baochang Zhang, Jungong Han, Chen Chen, Ling Shao, Alessandro Perina, Linlin Shen

There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling.

Object Localization

Boosting-like Deep Learning For Pedestrian Detection

no code implementations26 May 2015 Lei Wang, Baochang Zhang

This paper proposes boosting-like deep learning (BDL) framework for pedestrian detection.

Pedestrian Detection

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