Search Results for author: Jianzhuang Liu

Found 54 papers, 23 papers with code

Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery

no code implementations ECCV 2020 Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, Meng Wang

It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results.

Few-Shot Learning

Wavelet-Based Dual-Branch Network for Image Demoiréing

no code implementations ECCV 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Aleš Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

API-Net: Robust Generative Classifier via a Single Discriminator

1 code implementation ECCV 2020 Xinshuai Dong, Hong Liu, Rongrong Ji, Liujuan Cao, Qixiang Ye, Jianzhuang Liu, Qi Tian

On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure.

Robust classification

Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection

no code implementations19 May 2022 Zhuoling Li, Zhan Qu, Yang Zhou, Jianzhuang Liu, Haoqian Wang, Lihui Jiang

To tackle this problem, we propose a depth solving system that fully explores the visual clues from the subtasks in M3OD and generates multiple estimations for the depth of each target.

Depth Estimation Monocular 3D Object Detection

Prompt Distribution Learning

no code implementations6 May 2022 Yuning Lu, Jianzhuang Liu, Yonggang Zhang, Yajing Liu, Xinmei Tian

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks.

Language Modelling

Differentiated Relevances Embedding for Group-based Referring Expression Comprehension

no code implementations12 Mar 2022 Fuhai Chen, Xiaoshuai Sun, Xuri Ge, Jianzhuang Liu, Yongjian Wu, Feiyue Huang, Rongrong Ji

In particular, based on the visual and textual semantic features, RMSL conducts an adaptive learning cycle upon triplet ranking, where (1) the target-negative region-expression pairs with low within-group relevances are used preferentially in model training to distinguish the primary semantics of the target objects, and (2) an across-group relevance regularization is integrated into model training to balance the bias of group priority.

Referring Expression Referring Expression Comprehension

SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers

no code implementations17 Dec 2021 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Xin Guo, Youliang Yan, Qi Tian

For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders.

Denoising Frame +2

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

1 code implementation17 Nov 2021 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.

Quantization

Motion Deblurring with Real Events

no code implementations ICCV 2021 Fang Xu, Lei Yu, Bishan Wang, Wen Yang, Gui-Song Xia, Xu Jia, Zhendong Qiao, Jianzhuang Liu

In this paper, we propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner, where real-world events are exploited to alleviate the performance degradation caused by data inconsistency.

Deblurring

Wavelet-Based Network For High Dynamic Range Imaging

no code implementations3 Aug 2021 Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan

The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details.

Optical Flow Estimation

Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion

1 code implementation14 Jul 2021 Mingbao Lin, Rongrong Ji, Bohong Chen, Fei Chao, Jianzhuang Liu, Wei Zeng, Yonghong Tian, Qi Tian

Each filter in our DCFF is firstly given an inter-similarity distribution with a temperature parameter as a filter proxy, on top of which, a fresh Kullback-Leibler divergence based dynamic-coded criterion is proposed to evaluate the filter importance.

Image Classification

Multi-Target Domain Adaptation with Collaborative Consistency Learning

no code implementations CVPR 2021 Takashi Isobe, Xu Jia, Shuaijun Chen, Jianzhong He, Yongjie Shi, Jianzhuang Liu, Huchuan Lu, Shengjin Wang

To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights.

Multi-target Domain Adaptation Semantic Segmentation +1

Uformer: A General U-Shaped Transformer for Image Restoration

4 code implementations6 Jun 2021 Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, Houqiang Li

Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.

Deblurring Image Deblurring +6

Multiple instance active learning for object detection

1 code implementation CVPR 2021 Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, Qixiang Ye

Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection.

Active Object Detection Multiple Instance Learning +1

Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

4 code implementations CVPR 2021 Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, Jianzhuang Liu

In this paper, we present a very simple yet effective method named Neighbor2Neighbor to train an effective image denoising model with only noisy images.

Image Denoising Self-Supervised Learning

TRAR: Routing the Attention Spans in Transformer for Visual Question Answering

1 code implementation ICCV 2021 Yiyi Zhou, Tianhe Ren, Chaoyang Zhu, Xiaoshuai Sun, Jianzhuang Liu, Xinghao Ding, Mingliang Xu, Rongrong Ji

Due to the superior ability of global dependency modeling, Transformer and its variants have become the primary choice of many vision-and-language tasks.

Question Answering Referring Expression +3

Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs

no code implementations NeurIPS 2020 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

In this paper, we propose a self-adaptive learning method for demoiréing a high-frequency image, with the help of an additional defocused moiré-free blur image.

Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs

1 code implementation3 Nov 2020 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image.

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

Light Field View Synthesis via Aperture Disparity and Warping Confidence Map

no code implementations7 Sep 2020 Nan Meng, Kai Li, Jianzhuang Liu, Edmund Y. Lam

This paper presents a learning-based approach to synthesize the view from an arbitrary camera position given a sparse set of images.

Novel View Synthesis

Dual Distribution Alignment Network for Generalizable Person Re-Identification

1 code implementation27 Jul 2020 Peixian Chen, Pingyang Dai, Jianzhuang Liu, Feng Zheng, Qi Tian, Rongrong Ji

Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating.

Domain Generalization Generalizable Person Re-identification

Wavelet-Based Dual-Branch Network for Image Demoireing

no code implementations14 Jul 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

Projection & Probability-Driven Black-Box Attack

1 code implementation CVPR 2020 Jie Li, Rongrong Ji, Hong Liu, Jianzhuang Liu, Bineng Zhong, Cheng Deng, Qi Tian

For reducing the solution space, we first model the adversarial perturbation optimization problem as a process of recovering frequency-sparse perturbations with compressed sensing, under the setting that random noise in the low-frequency space is more likely to be adversarial.

High-Order Residual Network for Light Field Super-Resolution

1 code implementation29 Mar 2020 Nan Meng, Xiaofei Wu, Jianzhuang Liu, Edmund Y. Lam

In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction.

Super-Resolution

Context-Transformer: Tackling Object Confusion for Few-Shot Detection

1 code implementation16 Mar 2020 Ze Yang, Yali Wang, Xianyu Chen, Jianzhuang Liu, Yu Qiao

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors.

Few-Shot Learning Few-Shot Object Detection +1

Filter Sketch for Network Pruning

1 code implementation23 Jan 2020 Mingbao Lin, Liujuan Cao, Shaojie Li, Qixiang Ye, Yonghong Tian, Jianzhuang Liu, Qi Tian, Rongrong Ji

Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure.

Network Pruning

Multiple Anchor Learning for Visual Object Detection

3 code implementations CVPR 2020 Wei Ke, Tianliang Zhang, Zeyi Huang, Qixiang Ye, Jianzhuang Liu, Dong Huang

In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.

General Classification Multiple Instance Learning +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

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

An End-to-End Foreground-Aware Network for Person Re-Identification

no code implementations25 Oct 2019 Yiheng Liu, Wengang Zhou, Jianzhuang Liu, Guo-Jun Qi, Qi Tian, Houqiang Li

By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views.

Person Re-Identification

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).

Unsupervised Image Super-Resolution with an Indirect Supervised Path

no code implementations7 Oct 2019 Zhen Han, Enyan Dai, Xu Jia, Xiaoying Ren, Shuaijun Chen, Chunjing Xu, Jianzhuang Liu, Qi Tian

The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.

Image Super-Resolution Translation

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.

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

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.

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.

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

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.

A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition

no code implementations CVPR 2015 Dihong Gong, Zhifeng Li, DaCheng Tao, Jianzhuang Liu, Xuelong. Li

In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition.

Age-Invariant Face Recognition

Separation of Line Drawings Based on Split Faces for 3D Object Reconstruction

no code implementations CVPR 2014 Changqing Zou, Heng Yang, Jianzhuang Liu

Reconstructing 3D objects from single line drawings is often desirable in computer vision and graphics applications.

3D Object Reconstruction

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