Search Results for author: Zhikang Zou

Found 24 papers, 9 papers with code

Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis

1 code implementation3 Mar 2024 Xin Zhou, Dingkang Liang, Wei Xu, Xingkui Zhu, Yihan Xu, Zhikang Zou, Xiang Bai

To achieve this goal, we freeze the parameters of the default pre-trained models and then propose the Dynamic Adapter, which generates a dynamic scale for each token, considering the token significance to the downstream task.

Transfer Learning

AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Point Cloud Analysis

no code implementations27 Feb 2024 Hongcheng Yang, Dingkang Liang, Dingyuan Zhang, Xingyu Jiang, Zhe Liu, Zhikang Zou, Yingying Zhu

Specifically, we propose a Voxel Adaptation Module that adaptively adjusts voxel sizes with the reference of point-based downsampling ratio.

PointMamba: A Simple State Space Model for Point Cloud Analysis

1 code implementation16 Feb 2024 Dingkang Liang, Xin Zhou, Xinyu Wang, Xingkui Zhu, Wei Xu, Zhikang Zou, Xiaoqing Ye, Xiang Bai

Recently, state space models (SSM), a new family of deep sequence models, have presented great potential for sequence modeling in NLP tasks.

CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching

no code implementations6 Jul 2023 Jincheng Lu, Xipeng Yang, Jin Ye, Yifu Zhang, Zhikang Zou, Wei zhang, Xiao Tan

Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately.

Understanding Depth Map Progressively: Adaptive Distance Interval Separation for Monocular 3d Object Detection

no code implementations19 Jun 2023 Xianhui Cheng, Shoumeng Qiu, Zhikang Zou, Jian Pu, xiangyang xue

In this paper, we propose a framework named the Adaptive Distance Interval Separation Network (ADISN) that adopts a novel perspective on understanding depth maps, as a form that lies between LiDAR and images.

Depth Estimation Monocular 3D Object Detection +1

SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model

1 code implementation4 Jun 2023 Dingyuan Zhang, Dingkang Liang, Hongcheng Yang, Zhikang Zou, Xiaoqing Ye, Zhe Liu, Xiang Bai

In the spirit of unleashing the capability of foundation models on vision tasks, the Segment Anything Model (SAM), a vision foundation model for image segmentation, has been proposed recently and presents strong zero-shot ability on many downstream 2D tasks.

3D Object Detection Image Segmentation +3

Multi-Modal 3D Object Detection by Box Matching

1 code implementation12 May 2023 Zhe Liu, Xiaoqing Ye, Zhikang Zou, Xinwei He, Xiao Tan, Errui Ding, Jingdong Wang, Xiang Bai

Extensive experiments on the nuScenes dataset demonstrate that our method is much more stable in dealing with challenging cases such as asynchronous sensors, misaligned sensor placement, and degenerated camera images than existing fusion methods.

3D Object Detection Autonomous Driving +2

SOOD: Towards Semi-Supervised Oriented Object Detection

1 code implementation CVPR 2023 Wei Hua, Dingkang Liang, Jingyu Li, Xiaolong Liu, Zhikang Zou, Xiaoqing Ye, Xiang Bai

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years.

Object object-detection +4

Repainting and Imitating Learning for Lane Detection

no code implementations11 Oct 2022 Yue He, Minyue Jiang, Xiaoqing Ye, Liang Du, Zhikang Zou, Wei zhang, Xiao Tan, Errui Ding

In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.

Lane Detection

Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network

no code implementations12 Jul 2022 Bo Ju, Zhikang Zou, Xiaoqing Ye, Minyue Jiang, Xiao Tan, Errui Ding, Jingdong Wang

In this work, we propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models with the guidance of rich context painting, with no extra computation cost during inference.

3D Object Detection Autonomous Driving +1

SGM3D: Stereo Guided Monocular 3D Object Detection

1 code implementation3 Dec 2021 Zheyuan Zhou, Liang Du, Xiaoqing Ye, Zhikang Zou, Xiao Tan, Li Zhang, xiangyang xue, Jianfeng Feng

Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.

Autonomous Driving Depth Estimation +4

Self-Adaptive Partial Domain Adaptation

no code implementations18 Sep 2021 Jian Hu, Hongya Tuo, Shizhao Zhang, Chao Wang, Haowen Zhong, Zhikang Zou, Zhongliang Jing, Henry Leung, Ruping Zou

Partial Domain adaptation (PDA) aims to solve a more practical cross-domain learning problem that assumes target label space is a subset of source label space.

Partial Domain Adaptation

Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network

no code implementations27 Jul 2021 Zhikang Zou, Xiaoye Qu, Pan Zhou, Shuangjie Xu, Xiaoqing Ye, Wenhao Wu, Jin Ye

In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.

Crowd Counting Transfer Learning

Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation

no code implementations ICCV 2021 Zhi Chen, Xiaoqing Ye, Wei Yang, Zhenbo Xu, Xiao Tan, Zhikang Zou, Errui Ding, Xinming Zhang, Liusheng Huang

Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet.

Monocular Depth Estimation Stereo Matching

Crowd Counting via Hierarchical Scale Recalibration Network

no code implementations7 Mar 2020 Zhikang Zou, Yifan Liu, Shuangjie Xu, Wei Wei, Shiping Wen, Pan Zhou

Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches.

Crowd Counting

Enhanced 3D convolutional networks for crowd counting

no code implementations12 Aug 2019 Zhikang Zou, Huiliang Shao, Xiaoye Qu, Wei Wei, Pan Zhou

Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting.

Crowd Counting

Adversarial Category Alignment Network for Cross-domain Sentiment Classification

no code implementations NAACL 2019 Xiaoye Qu, Zhikang Zou, Yu Cheng, Yang Yang, Pan Zhou

Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain.

Classification General Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.