Search Results for author: Yuenan Hou

Found 31 papers, 20 papers with code

OccMamba: Semantic Occupancy Prediction with State Space Models

no code implementations19 Aug 2024 Heng Li, Yuenan Hou, Xiaohan Xing, Xiao Sun, Yanyong Zhang

Inspired by the global modeling and linear computation complexity of the Mamba architecture, we present the first Mamba-based network for semantic occupancy prediction, termed OccMamba.

Mamba State Space Models

Semi-supervised 3D Object Detection with PatchTeacher and PillarMix

no code implementations13 Jul 2024 Xiaopei Wu, Liang Peng, Liang Xie, Yuenan Hou, Binbin Lin, Xiaoshui Huang, Haifeng Liu, Deng Cai, Wanli Ouyang

In this paper, we propose PatchTeacher, which focuses on partial scene 3D object detection to provide high-quality pseudo labels for the student.

3D Object Detection Data Augmentation +2

SARatrX: Towards Building A Foundation Model for SAR Target Recognition

2 code implementations15 May 2024 Weijie Li, Wei Yang, Yuenan Hou, Li Liu, Yongxiang Liu, Xiang Li

Despite the remarkable progress in synthetic aperture radar automatic target recognition (SAR ATR), recent efforts have concentrated on the detection or classification of a specific and coarse category, e. g., vehicles, ships, airplanes, or buildings.

Earth Observation Self-Supervised Learning

NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection

1 code implementation22 Feb 2024 Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, Deng Cai, Wanli Ouyang

We project the freely available 3D segmentation annotations onto the 2D plane and leverage the corresponding 2D semantic maps as the supervision signal, significantly enhancing the semantic awareness of multi-view detectors.

Depth Estimation Depth Prediction +1

A Comprehensive Survey on 3D Content Generation

1 code implementation2 Feb 2024 Jian Liu, Xiaoshui Huang, Tianyu Huang, Lu Chen, Yuenan Hou, Shixiang Tang, Ziwei Liu, Wanli Ouyang, WangMeng Zuo, Junjun Jiang, Xianming Liu

Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e. g., text, image, video, audio and 3D.

Survey

Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture

2 code implementations26 Nov 2023 Weijie Li, Yang Wei, Tianpeng Liu, Yuenan Hou, YuXuan Li, Zhen Liu, Yongxiang Liu, Li Liu

The growing Synthetic Aperture Radar (SAR) data has the potential to build a foundation model through Self-Supervised Learning (SSL) methods, which can achieve various SAR Automatic Target Recognition (ATR) tasks with pre-training in large-scale unlabeled data and fine-tuning in small labeled samples.

Representation Learning Self-Supervised Learning

Point Cloud Pre-training with Diffusion Models

no code implementations CVPR 2024 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

1 code implementation12 Apr 2023 Zhenxiang Lin, Xidong Peng, Peishan Cong, Ge Zheng, Yujin Sun, 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.

3D visual grounding Autonomous Driving +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

EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder

2 code implementations8 Dec 2022 Xiaoshui Huang, Zhou Huang, Sheng Li, Wentao Qu, Tong He, Yuenan Hou, Yifan Zuo, Wanli Ouyang

These token embeddings are concatenated with a task token and fed into the frozen CLIP transformer to learn point cloud representation.

Few-Shot Learning Segmentation +1

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.

Survey

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.

3D Object Detection Pedestrian Detection +1

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 Continuous Control +1

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