Search Results for author: Jianing Li

Found 24 papers, 7 papers with code

XPose: eXplainable Human Pose Estimation

no code implementations19 Mar 2024 Luyu Qiu, Jianing Li, Lei Wen, Chi Su, Fei Hao, Chen Jason Zhang, Lei Chen

In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation.

Computational Efficiency Data Augmentation +1

Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis

no code implementations31 Jan 2024 Jianing Li, Xi Nan, Ming Lu, Li Du, Shanghang Zhang

To overcome this limitation in MLLMs, we introduce Proximity Question Answering (Proximity QA), a novel framework designed to enable MLLMs to infer the proximity relationship between objects in images.

Multi-Task Learning Question Answering +1

Residual Alignment: Uncovering the Mechanisms of Residual Networks

no code implementations NeurIPS 2023 Jianing Li, Vardan Papyan

Our measurements reveal a process called Residual Alignment (RA) characterized by four properties: (RA1) intermediate representations of a given input are equispaced on a line, embedded in high dimensional space, as observed by Gai and Zhang [2021]; (RA2) top left and right singular vectors of Residual Jacobians align with each other and across different depths; (RA3) Residual Jacobians are at most rank C for fully-connected ResNets, where C is the number of classes; and (RA4) top singular values of Residual Jacobians scale inversely with depth.

SODFormer: Streaming Object Detection with Transformer Using Events and Frames

1 code implementation8 Aug 2023 Dianze Li, Jianing Li, Yonghong Tian

Then, we design a spatiotemporal Transformer architecture to detect objects via an end-to-end sequence prediction problem, where the novel temporal Transformer module leverages rich temporal cues from two visual streams to improve the detection performance.

object-detection Object Detection

Deep Directly-Trained Spiking Neural Networks for Object Detection

1 code implementation ICCV 2023 Qiaoyi Su, Yuhong Chou, Yifan Hu, Jianing Li, Shijie Mei, Ziyang Zhang, Guoqi Li

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.

Object object-detection +1

Full Resolution Repetition Counting

no code implementations23 May 2023 Jianing Li, Bowen Chen, Zhiyong Wang, Honghai Liu

Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions.

Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems

no code implementations22 May 2023 Luzhe Huang, Jianing Li, Xiaofu Ding, Yijie Zhang, Hanlong Chen, Aydogan Ozcan

Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers.

Deblurring Image Deblurring +2

Event-based Monocular Dense Depth Estimation with Recurrent Transformers

no code implementations6 Dec 2022 Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian

Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e. g., motion blur and low light) in monocular depth estimation.

Event-based vision Monocular Depth Estimation

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

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.

Computational Efficiency Event-Based Video Reconstruction +2

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

Concerted Rolling and Membrane Penetration Revealed by Atomistic Simulations of Antimicrobial Peptides

no code implementations3 Nov 2021 Jacob M. Remington, Jonathon B. Ferrell, Jianing Li

Short peptides with antimicrobial activity have therapeutic potential for treating bacterial infections.

VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows

2 code implementations11 Aug 2021 Xiao Wang, Jianing Li, Lin Zhu, Zhipeng Zhang, Zhe Chen, Xin Li, YaoWei Wang, Yonghong Tian, Feng Wu

Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency.

Object Tracking

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise

no code implementations NeurIPS 2021 Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger Grosse

To this end, we propose Differentiable AIS (DAIS), a variant of AIS which ensures differentiability by abandoning the Metropolis-Hastings corrections.

Stochastic Optimization

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

Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification

no code implementations ECCV 2020 Jianing Li, Shiliang Zhang

This paper tackles this challenge through jointly enforcing visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification.

Classification Domain Adaptive Person Re-Identification +3

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation

no code implementations ICML 2020 Jianing Li, Yanyan Lan, Jiafeng Guo, Xue-Qi Cheng

We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution.

Relation Text Generation

Improving Generalizability of Fake News Detection Methods using Propensity Score Matching

1 code implementation28 Jan 2020 Bo Ni, Zhichun Guo, Jianing Li, Meng Jiang

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public.

Fake News Detection regression

Global-Local Temporal Representations For Video Person Re-Identification

no code implementations ICCV 2019 Jianing Li, Jingdong Wang, Qi Tian, Wen Gao, Shiliang Zhang

The long-term relations are captured by a temporal self-attention model to alleviate the occlusions and noises in video sequences.

Metric Learning Re-Ranking +1

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

LVreID: Person Re-Identification with Long Sequence Videos

no code implementations20 Dec 2017 Jianing Li, Shiliang Zhang, Jingdong Wang, Wen Gao, Qi Tian

This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID).

Person Re-Identification

Pose-driven Deep Convolutional Model for Person Re-identification

no code implementations ICCV 2017 Chi Su, Jianing Li, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian

Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.

Person Re-Identification

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