Search Results for author: Tiejun Huang

Found 59 papers, 13 papers with code

An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification

no code implementations ECCV 2020 Zilong Ji, Xiaolong Zou, Xiaohan Lin, Xiao Liu, Tiejun Huang, Si Wu

By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative.

Unsupervised Person Re-Identification

Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection

no code implementations10 Mar 2022 Lantian Xue, Yixiong Zou, Peixi Peng, Yonghong Tian, Tiejun Huang

To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation.

Person Re-Identification

Optimized Potential Initialization for Low-latency Spiking Neural Networks

no code implementations3 Feb 2022 Tong Bu, Jianhao Ding, Zhaofei Yu, Tiejun Huang

We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps.

Adversarial Robustness

Event-based Video Reconstruction via Potential-assisted Spiking Neural Network

no code implementations25 Jan 2022 Lin Zhu, Xiao Wang, Yi Chang, Jianing Li, Tiejun Huang, Yonghong Tian

We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.

Image Reconstruction Video Reconstruction

1000x Faster Camera and Machine Vision with Ordinary Devices

no code implementations23 Jan 2022 Tiejun Huang, Yajing Zheng, Zhaofei Yu, Rui Chen, Yuan Li, Ruiqin Xiong, Lei Ma, Junwei Zhao, Siwei Dong, Lin Zhu, Jianing Li, Shanshan Jia, Yihua Fu, Boxin Shi, Si Wu, Yonghong Tian

By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.

Object Detection

Deep Reinforcement Learning with Spiking Q-learning

no code implementations21 Jan 2022 Ding Chen, Peixi Peng, Tiejun Huang, Yonghong Tian

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence with less energy consumption.

Atari Games Q-Learning +1

A Robust Visual Sampling Model Inspired by Receptive Field

no code implementations4 Jan 2022 Liwen Hu, Lei Ma, Dawei Weng, Tiejun Huang

More importantly, due to mimicking receptive field mechanism to collect regional information, RVSM can filter high intensity noise effectively and improves the problem that Spike camera is sensitive to noise largely.

Quantization

Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

1 code implementation2 Dec 2021 Wenkai Chen, Chuang Zhu, Yi Chen, Mengting Li, Tiejun Huang

Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance.

 Ranked #1 on Image Classification on Clothing1M (using extra training data)

Learning with noisy labels

Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks

no code implementations NeurIPS 2021 Xingsi Dong, Tianhao Chu, Tiejun Huang, Zilong Ji, Si Wu

To elucidate the underlying mechanism clearly, we first study continuous attractor neural networks (CANNs), and find that noisy neural adaptation, exemplified by spike frequency adaptation (SFA) in this work, can generate Lévy flights representing transitions of the network state in the attractor space.

Optical Flow Estimation for Spiking Camera

no code implementations8 Oct 2021 Liwen Hu, Rui Zhao, Ziluo Ding, Lei Ma, Boxin Shi, Ruiqin Xiong, Tiejun Huang

Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes.

Event-based vision Frame +2

Accelerating Training of Deep Spiking Neural Networks with Parameter Initialization

no code implementations29 Sep 2021 Jianhao Ding, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang

Despite that spiking neural networks (SNNs) show strong advantages in information encoding, power consuming, and computational capability, the underdevelopment of supervised learning algorithms is still a hindrance for training SNN.

Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation

1 code implementation10 Sep 2021 Ziluo Ding, Rui Zhao, Jiyuan Zhang, Tianxiao Gao, Ruiqin Xiong, Zhaofei Yu, Tiejun Huang

Recently, many deep learning methods have shown great success in providing promising solutions to many event-based problems, such as optical flow estimation.

Frame Optical Flow Estimation +1

Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream

no code implementations CVPR 2021 Jing Zhao, Ruiqin Xiong, Hangfan Liu, Jian Zhang, Tiejun Huang

Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process.

Image Reconstruction

High-Speed Image Reconstruction Through Short-Term Plasticity for Spiking Cameras

no code implementations CVPR 2021 Yajing Zheng, Lingxiao Zheng, Zhaofei Yu, Boxin Shi, Yonghong Tian, Tiejun Huang

Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40, 000 Hz, and outputs asynchronous binary spike streams.

Image Reconstruction

Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks

1 code implementation25 May 2021 Jianhao Ding, Zhaofei Yu, Yonghong Tian, Tiejun Huang

We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference.

Pruning of Deep Spiking Neural Networks through Gradient Rewiring

1 code implementation11 May 2021 Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, Yonghong Tian

Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections.

Deep Residual Learning in Spiking Neural Networks

1 code implementation NeurIPS 2021 Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, Yonghong Tian

Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning.

NeuSpike-Net: High Speed Video Reconstruction via Bio-Inspired Neuromorphic Cameras

no code implementations ICCV 2021 Lin Zhu, Jianing Li, Xiao Wang, Tiejun Huang, Yonghong Tian

In this paper, we propose a NeuSpike-Net to learn both the high dynamic range and high motion sensitivity of DVS and the full texture sampling of spike camera to achieve high-speed and high dynamic image reconstruction.

Image Reconstruction Video Reconstruction

Super Resolve Dynamic Scene From Continuous Spike Streams

no code implementations ICCV 2021 Jing Zhao, Jiyu Xie, Ruiqin Xiong, Jian Zhang, Zhaofei Yu, Tiejun Huang

In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution.

Super-Resolution

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging

no code implementations NeurIPS 2020 Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi

A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).

Learning Open Set Network with Discriminative Reciprocal Points

1 code implementation ECCV 2020 Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, ShiLiang Pu, Yonghong Tian

In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.

Open Set Learning

Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways

no code implementations23 Aug 2020 Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu

In this study, we build a computational model to elucidate the computational advantages associated with the interactions between two pathways.

Object Recognition

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

1 code implementation ICCV 2021 Wei Fang, Zhaofei Yu, Yanqi Chen, Timothee Masquelier, Tiejun Huang, Yonghong Tian

In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs.

Image Classification

Learning Individually Inferred Communication for Multi-Agent Cooperation

1 code implementation NeurIPS 2020 Ziluo Ding, Tiejun Huang, Zongqing Lu

Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods.

Causal Inference Multi-agent Reinforcement Learning

Kernel Quantization for Efficient Network Compression

no code implementations11 Mar 2020 Zhongzhi Yu, Yemin Shi, Tiejun Huang, Yizhou Yu

Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.

Quantization

Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics

no code implementations10 Jan 2020 Ling-Yu Duan, Jiaying Liu, Wenhan Yang, Tiejun Huang, Wen Gao

Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications.

Video Compression

Unsupervised Few-shot Learning via Self-supervised Training

no code implementations20 Dec 2019 Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu

The proposed model consists of two alternate processes, progressive clustering and episodic training.

Few-Shot Learning Person Re-Identification

Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently

1 code implementation NeurIPS 2019 Xiao Liu, Xiaolong Zou, Zilong Ji, Gengshuo Tian, Yuanyuan Mi, Tiejun Huang, K. Y. Michael Wong, Si Wu

Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway.

Information Retrieval

Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning

no code implementations ICCV 2019 Limeng Qiao, Yemin Shi, Jia Li, Yao-Wei Wang, Tiejun Huang, Yonghong Tian

By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.

Few-Shot Learning Metric Learning

Knowledge Transfer via Student-Teacher Collaboration

no code implementations25 Sep 2019 Tianxiao Gao, Ruiqin Xiong, Zhenhua Liu, Siwei Ma, Feng Wu, Tiejun Huang, Wen Gao

One way to compress these heavy models is knowledge transfer (KT), in which a light student network is trained through absorbing the knowledge from a powerful teacher network.

Transfer Learning

Unsupervised Few Shot Learning via Self-supervised Training

no code implementations25 Sep 2019 Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu

Using the benchmark dataset Omniglot, we show that our model outperforms other unsupervised few-shot learning methods to a large extend and approaches to the performances of supervised methods.

Few-Shot Learning Person Re-Identification

A Retina-inspired Sampling Method for Visual Texture Reconstruction

no code implementations20 Jul 2019 Lin Zhu, Siwei Dong, Tiejun Huang, Yonghong Tian

Conventional frame-based camera is not able to meet the demand of rapid reaction for real-time applications, while the emerging dynamic vision sensor (DVS) can realize high speed capturing for moving objects.

Frame

P-ODN: Prototype based Open Deep Network for Open Set Recognition

no code implementations6 May 2019 Yu Shu, Yemin Shi, Yao-Wei Wang, Tiejun Huang, Yonghong Tian

Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically.

Open Set Learning

Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks

no code implementations30 Apr 2019 Yichen Zhang, Shanshan Jia, Yajing Zheng, Zhaofei Yu, Yonghong Tian, Siwei Ma, Tiejun Huang, Jian. K. Liu

The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion.

Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation

no code implementations22 Feb 2019 Yajing Zheng, Shanshan Jia, Zhaofei Yu, Tiejun Huang, Jian. K. Liu, Yonghong Tian

Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields.

Image Denoising

Multi-scale 3D Convolution Network for Video Based Person Re-Identification

no code implementations19 Nov 2018 Jianing Li, Shiliang Zhang, Tiejun Huang

A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network.

Video-Based Person Re-Identification

Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks

no code implementations6 Nov 2018 Qi Yan, Yajing Zheng, Shanshan Jia, Yichen Zhang, Zhaofei Yu, Feng Chen, Yonghong Tian, Tiejun Huang, Jian. K. Liu

When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.

Transfer Learning

Neural System Identification with Spike-triggered Non-negative Matrix Factorization

no code implementations12 Aug 2018 Shanshan Jia, Zhaofei Yu, Arno Onken, Yonghong Tian, Tiejun Huang, Jian. K. Liu

Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell.

Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits

no code implementations2 Aug 2018 Zhaofei Yu, Yonghong Tian, Tiejun Huang, Jian. K. Liu

Taken together, our results suggest that the WTA circuit could be seen as the minimal inference unit of neuronal circuits.

Bayesian Inference

Depth-Aware Stereo Video Retargeting

no code implementations CVPR 2018 Bing Li, Chia-Wen Lin, Boxin Shi, Tiejun Huang, Wen Gao, C. -C. Jay Kuo

As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics.

E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network

no code implementations18 Sep 2017 Xiaobin Liu, Shiliang Zhang, Tiejun Huang, Qi Tian

To conquer these issues, we propose an End-to-End BoWs (E$^2$BoWs) model based on Deep Convolutional Neural Network (DCNN).

Image Retrieval Quantization

Compact Descriptors for Video Analysis: the Emerging MPEG Standard

no code implementations26 Apr 2017 Ling-Yu Duan, Vijay Chandrasekhar, Shiqi Wang, Yihang Lou, Jie Lin, Yan Bai, Tiejun Huang, Alex ChiChung Kot, Wen Gao

This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).

Improving Object Detection with Region Similarity Learning

no code implementations1 Mar 2017 Feng Gao, Yihang Lou, Yan Bai, Shiqi Wang, Tiejun Huang, Ling-Yu Duan

Object detection aims to identify instances of semantic objects of a certain class in images or videos.

Multi-Task Learning Object Detection

Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

no code implementations1 Mar 2017 Yan Bai, Feng Gao, Yihang Lou, Shiqi Wang, Tiejun Huang, Ling-Yu Duan

In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.

Fine-Grained Visual Recognition Metric Learning

Joint Network based Attention for Action Recognition

no code implementations16 Nov 2016 Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang

We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions.

Action Recognition

Learning long-term dependencies for action recognition with a biologically-inspired deep network

1 code implementation ICCV 2017 Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang

Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task.

Action Recognition

Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN

no code implementations10 Sep 2016 Yemin Shi, Yonghong Tian, Yao-Wei Wang, Tiejun Huang

Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.

Action Recognition

Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification

no code implementations CVPR 2016 Peixi Peng, Tao Xiang, Yao-Wei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.

Dictionary Learning Person Re-Identification +1

Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles

no code implementations CVPR 2016 Hongye Liu, Yonghong Tian, Yaowei Yang, Lu Pang, Tiejun Huang

To further facilitate the future research on this problem, we also present a carefully-organized large-scale image database "VehicleID", which includes multiple images of the same vehicle captured by different real-world cameras in a city.

Face Recognition Person Re-Identification +1

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