Search Results for author: Tiejun Huang

Found 86 papers, 28 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.

Clustering Unsupervised Person Re-Identification

Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera

no code implementations12 Jul 2023 Lujie Xia, Ziluo Ding, Rui Zhao, Jiyuan Zhang, Lei Ma, Zhaofei Yu, Tiejun Huang, Ruiqin Xiong

Moreover, we propose an unsupervised learning method for optical flow estimation in a spike-based manner to break the dependence on labeled data.

Autonomous Driving Optical Flow Estimation

Generative Pretraining in Multimodality

2 code implementations11 Jul 2023 Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, Xinlong Wang

We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context.

Ranked #5 on Temporal/Casual QA on NExT-QA (using extra training data)

Image Captioning Temporal/Casual QA +3

SVIT: Scaling up Visual Instruction Tuning

1 code implementation9 Jul 2023 Bo Zhao, Boya Wu, Tiejun Huang

Thanks to the emerging of foundation models, the large language and vision models are integrated to acquire the multimodal ability of visual captioning, dialogue, question answering, etc.

Image Captioning Question Answering

Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views

no code implementations3 Jul 2023 Jiyuan Zhang, Shiyan Chen, Yajing Zheng, Zhaofei Yu, Tiejun Huang

To process the spikes, we build a novel model \textbf{SpkOccNet}, in which we integrate information of spikes from continuous viewpoints within multi-windows, and propose a novel cross-view mutual attention mechanism for effective fusion and refinement.

Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity

no code implementations21 Jun 2023 Xundong Wu, Pengfei Zhao, Zilin Yu, Lei Ma, Ka-Wa Yip, Huajin Tang, Gang Pan, Tiejun Huang

Our comprehension of biological neuronal networks has profoundly influenced the evolution of artificial neural networks (ANNs).

Pushing the Limits of 3D Shape Generation at Scale

no code implementations20 Jun 2023 Yu Wang, Xuelin Qian, Jingyang Huo, Tiejun Huang, Bo Zhao, Yanwei Fu

Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.

3D Shape Generation Quantization +1

Spike timing reshapes robustness against attacks in spiking neural networks

no code implementations9 Jun 2023 Jianhao Ding, Zhaofei Yu, Tiejun Huang, Jian K. Liu

The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks.

Large-scale Dataset Pruning with Dynamic Uncertainty

1 code implementation8 Jun 2023 Muyang He, Shuo Yang, Tiejun Huang, Bo Zhao

The state of the art of many learning tasks, e. g., image classification, is advanced by collecting larger datasets and then training larger models on them.

Image Classification

HUB: Guiding Learned Optimizers with Continuous Prompt Tuning

no code implementations26 May 2023 Gaole Dai, Wei Wu, Ziyu Wang, Jie Fu, Shanghang Zhang, Tiejun Huang

By incorporating hand-designed optimizers as the second component in our hybrid approach, we are able to retain the benefits of learned optimizers while stabilizing the training process and, more importantly, improving testing performance.


Spike Stream Denoising via Spike Camera Simulation

no code implementations6 Apr 2023 Liwen Hu, Lei Ma, Zhaofei Yu, Boxin Shi, Tiejun Huang

Based on our noise model, the first benchmark for spike stream denoising is proposed which includes clear (noisy) spike stream.


SegGPT: Segmenting Everything In Context

1 code implementation6 Apr 2023 Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang

We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.

Few-Shot Semantic Segmentation Panoptic Segmentation +3

Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios

no code implementations29 Mar 2023 Shiyan Chen, Jiyuan Zhang, Zhaofei Yu, Tiejun Huang

Self-supervised denoising has attracted widespread attention due to its ability to train without clean images.


SpikeCV: Open a Continuous Computer Vision Era

1 code implementation21 Mar 2023 Yajing Zheng, Jiyuan Zhang, Rui Zhao, Jianhao Ding, Shiyan Chen, Ruiqin Xiong, Zhaofei Yu, Tiejun Huang

SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes.

EVA-02: A Visual Representation for Neon Genesis

5 code implementations20 Mar 2023 Yuxin Fang, Quan Sun, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao

We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling.

CLIP4MC: An RL-Friendly Vision-Language Model for Minecraft

no code implementations19 Mar 2023 Ziluo Ding, Hao Luo, Ke Li, Junpeng Yue, Tiejun Huang, Zongqing Lu

One of the essential missions in the AI research community is to build an autonomous embodied agent that can attain high-level performance across a wide spectrum of tasks.

Contrastive Learning Language Modelling +1

Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes

2 code implementations21 Feb 2023 Zecheng Hao, Jianhao Ding, Tong Bu, Tiejun Huang, Zhaofei Yu

The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets.

Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation

1 code implementation14 Feb 2023 Chuang Zhu, Kebin Liu, Wenqi Tang, Ke Mei, Jiaqi Zou, Tiejun Huang

The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models.

Model Optimization Pseudo Label +2

Reducing ANN-SNN Conversion Error through Residual Membrane Potential

2 code implementations4 Feb 2023 Zecheng Hao, Tong Bu, Jianhao Ding, Tiejun Huang, Zhaofei Yu

Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips.

Temporal Sequences

MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario

no code implementations ICCV 2023 Zhixuan Li, Weining Ye, Juan Terven, Zachary Bennett, Ying Zheng, Tingting Jiang, Tiejun Huang

To bridge this gap, we propose a new task called Multi-view Amodal Instance Segmentation (MAIS) and introduce the MUVA dataset, the first MUlti-View AIS dataset that takes the shopping scenario as instantiation.

1000 FPS HDR Video With a Spike-RGB Hybrid Camera

no code implementations CVPR 2023 Yakun Chang, Chu Zhou, Yuchen Hong, Liwen Hu, Chao Xu, Tiejun Huang, Boxin Shi

Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging.

Video Reconstruction

SegGPT: Towards Segmenting Everything in Context

no code implementations ICCV 2023 Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang

We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.

Images Speak in Images: A Generalist Painter for In-Context Visual Learning

1 code implementation CVPR 2023 Xinlong Wang, Wen Wang, Yue Cao, Chunhua Shen, Tiejun Huang

In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images.

Keypoint Detection Personalized Segmentation +1

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

6 code implementations CVPR 2023 Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao

We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.

 Ranked #1 on Object Detection on COCO-O (using extra training data)

Action Classification Action Recognition +8

Multi-Agent Sequential Decision-Making via Communication

no code implementations26 Sep 2022 Ziluo Ding, Kefan Su, Weixin Hong, Liwen Zhu, Tiejun Huang, Zongqing Lu

Communication helps agents to obtain information about others so that better coordinated behavior can be learned.

Decision Making

Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer

1 code implementation26 Aug 2022 Jiaming Liu, Qizhe Zhang, Jianing Li, Ming Lu, Tiejun Huang, Shanghang Zhang

Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in real-world applications due to its inherent advantage to overcome high-velocity motion blur.

Autonomous Driving Depth Estimation +2

Uncertainty Guided Depth Fusion for Spike Camera

no code implementations26 Aug 2022 Jianing Li, Jiaming Liu, Xiaobao Wei, Jiyuan Zhang, Ming Lu, Lei Ma, Li Du, Tiejun Huang, Shanghang Zhang

In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.

Autonomous Driving Stereo Depth Estimation

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.

Clustering 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

1 code implementation CVPR 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 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 +2

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.


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

1 code implementation CVPR 2022 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 Motion Estimation +1

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.

Event-based Optical Flow Optical Flow Estimation +1

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 Vocal Bursts Intensity Prediction

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

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.

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.


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 +1

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.


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.

Feature Compression 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.

BIG-bench Machine Learning Clustering +3

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

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.

Person Re-Identification Unsupervised Few-Shot 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

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.

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.

Bi-Directional Cascade Network for Perceptual Edge Detection

2 code implementations CVPR 2019 Jianzhong He, Shiliang Zhang, Ming Yang, Yanhu Shan, Tiejun Huang

Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.

Edge Detection

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 +1

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 +1

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 +1

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 Temporal Action Localization

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 Temporal Action Localization

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 Temporal Action Localization

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 +2

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