Search Results for author: Yuenan Hou

Found 24 papers, 15 papers with code

Self-Supervised Learning for SAR ATR with a Knowledge-Guided Predictive Architecture

no code implementations26 Nov 2023 Weijie Li, Yang Wei, Tianpeng Liu, Yuenan Hou, Yongxiang Liu, Li Liu

Recently, the emergence of a large number of Synthetic Aperture Radar (SAR) sensors and target datasets has made it possible to unify downstream tasks with self-supervised learning techniques, which can pave the way for building the foundation model in the SAR target recognition field.

Representation Learning Self-Supervised Learning

Point Cloud Pre-training with Diffusion Models

no code implementations25 Nov 2023 Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, Yongshun Gong

This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object.

Point Cloud Pre-training

Human-centric Scene Understanding for 3D Large-scale Scenarios

1 code implementation ICCV 2023 Yiteng Xu, Peishan Cong, Yichen Yao, Runnan Chen, Yuenan Hou, Xinge Zhu, Xuming He, Jingyi Yu, Yuexin Ma

Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human-environment interactions, severe occlusions in crowds, etc.

Action Recognition Scene Understanding +1

WildRefer: 3D Object Localization in Large-scale Dynamic Scenes with Multi-modal Visual Data and Natural Language

no code implementations12 Apr 2023 Zhenxiang Lin, Xidong Peng, Peishan Cong, Yuenan Hou, Xinge Zhu, Sibei Yang, Yuexin Ma

We introduce the task of 3D visual grounding in large-scale dynamic scenes based on natural linguistic descriptions and online captured multi-modal visual data, including 2D images and 3D LiDAR point clouds.

Autonomous Driving Object Localization +1

SCPNet: Semantic Scene Completion on Point Cloud

1 code implementation CVPR 2023 Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao

We propose a simple yet effective label rectification strategy, which uses off-the-shelf panoptic segmentation labels to remove the traces of dynamic objects in completion labels, greatly improving the performance of deep models especially for those moving objects.

3D Semantic Scene Completion Knowledge Distillation +3

Rethinking Range View Representation for LiDAR Segmentation

no code implementations ICCV 2023 Lingdong Kong, Youquan Liu, Runnan Chen, Yuexin Ma, Xinge Zhu, Yikang Li, Yuenan Hou, Yu Qiao, Ziwei Liu

We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i. e., SemanticKITTI, nuScenes, and ScribbleKITTI.

3D Semantic Segmentation Autonomous Driving +4

CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP

1 code implementation CVPR 2023 Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, Wenping Wang

For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20. 8% and 25. 08% mIoU on nuScenes and ScanNet, respectively.

3D Semantic Segmentation Contrastive Learning +4

Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection

no code implementations18 Oct 2022 Xin Li, Botian Shi, Yuenan Hou, Xingjiao Wu, Tianlong Ma, Yikang Li, Liang He

To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space.

3D Object Detection Autonomous Driving +1

Mind the Gap in Distilling StyleGANs

1 code implementation18 Aug 2022 Guodong Xu, Yuenan Hou, Ziwei Liu, Chen Change Loy

To further enhance the semantic consistency between the teacher and student model, we present a latent-direction-based distillation loss that preserves the semantic relations in latent space.

Knowledge Distillation

Vision-Centric BEV Perception: A Survey

1 code implementation4 Aug 2022 Yuexin Ma, Tai Wang, Xuyang Bai, Huitong Yang, Yuenan Hou, Yaming Wang, Yu Qiao, Ruigang Yang, Dinesh Manocha, Xinge Zhu

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion.

Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation

1 code implementation CVPR 2022 Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, Yikang Li

This article addresses the problem of distilling knowledge from a large teacher model to a slim student network for LiDAR semantic segmentation.

Ranked #5 on LIDAR Semantic Segmentation on nuScenes (val mIoU metric)

3D Semantic Segmentation Knowledge Distillation +1

STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

1 code implementation CVPR 2022 Peishan Cong, Xinge Zhu, Feng Qiao, Yiming Ren, Xidong Peng, Yuenan Hou, Lan Xu, Ruigang Yang, Dinesh Manocha, Yuexin Ma

In addition, considering the property of sparse global distribution and density-varying local distribution of pedestrians, we further propose a novel method, Density-aware Hierarchical heatmap Aggregation (DHA), to enhance pedestrian perception in crowded scenes.

Pedestrian Detection Sensor Fusion

A Comprehensive Overhaul of Distilling Unconditional GANs

no code implementations29 Sep 2021 Guodong Xu, Yuenan Hou, Ziwei Liu, Chen Change Loy

To further enhance the semantic consistency between the teacher and student model, we present another latent-direction-based distillation loss that preserves the semantic relations in latent space.

Knowledge Distillation

Network Pruning via Resource Reallocation

1 code implementation2 Mar 2021 Yuenan Hou, Zheng Ma, Chunxiao Liu, Zhe Wang, Chen Change Loy

Channel pruning is broadly recognized as an effective approach to obtain a small compact model through eliminating unimportant channels from a large cumbersome network.

Network Pruning

Inter-Region Affinity Distillation for Road Marking Segmentation

1 code implementation CVPR 2020 Yuenan Hou, Zheng Ma, Chunxiao Liu, Tak-Wai Hui, Chen Change Loy

We study the problem of distilling knowledge from a large deep teacher network to a much smaller student network for the task of road marking segmentation.

Knowledge Distillation Lane Detection +1

Learning Lightweight Lane Detection CNNs by Self Attention Distillation

2 code implementations ICCV 2019 Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy

Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations.

Knowledge Distillation Lane Detection +1

Agnostic Lane Detection

no code implementations2 May 2019 Yuenan Hou

Lane detection is an important yet challenging task in autonomous driving, which is affected by many factors, e. g., light conditions, occlusions caused by other vehicles, irrelevant markings on the road and the inherent long and thin property of lanes.

Autonomous Driving Instance Segmentation +3

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

2 code implementations7 Nov 2018 Yuenan Hou, Zheng Ma, Chunxiao Liu, Chen Change Loy

In this paper, we considerably improve the accuracy and robustness of predictions through heterogeneous auxiliary networks feature mimicking, a new and effective training method that provides us with much richer contextual signals apart from steering direction.

Image Segmentation Multi-Task Learning +3

A novel DDPG method with prioritized experience replay

1 code implementation IEEE International Conference on Systems, Man and Cybernetics (SMC) 2017 Yuenan Hou, Lifeng Liu, Qing Wei, Xudong Xu, Chunlin Chen

Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator.

Continuous Control OpenAI Gym

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