Search Results for author: Gangshan Wu

Found 52 papers, 39 papers with code

Sketch and Refine: Towards Fast and Accurate Lane Detection

no code implementations26 Jan 2024 Chao Chen, Jie Liu, Chang Zhou, Jie Tang, Gangshan Wu

At the "Sketch" stage, local directions of keypoints can be easily estimated by fast convolutional layers.

Lane Detection

Asymmetric Masked Distillation for Pre-Training Small Foundation Models

no code implementations6 Nov 2023 Zhiyu Zhao, Bingkun Huang, Sen Xing, Gangshan Wu, Yu Qiao, LiMin Wang

And AMD achieves 73. 3% classification accuracy using the ViT-B model on the Something-in-Something V2 dataset, a 3. 7% improvement over the original ViT-B model from VideoMAE.

Knowledge Distillation Model Compression

Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation

no code implementations25 Aug 2023 Jiaming Zhang, Yutao Cui, Gangshan Wu, LiMin Wang

To overcome these issues, we propose a unified VOS framework, coined as JointFormer, for joint modeling the three elements of feature, correspondence, and a compressed memory.

Semantic Segmentation Video Object Segmentation +1

DPL: Decoupled Prompt Learning for Vision-Language Models

no code implementations19 Aug 2023 Chen Xu, Yuhan Zhu, Guozhen Zhang, Haocheng Shen, Yixuan Liao, Xiaoxin Chen, Gangshan Wu, LiMin Wang

Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e. g., CLIP) to downstream tasks.

Robust Object Modeling for Visual Tracking

1 code implementation ICCV 2023 Yidong Cai, Jie Liu, Jie Tang, Gangshan Wu

To enjoy the merits of both methods, we propose a robust object modeling framework for visual tracking (ROMTrack), which simultaneously models the inherent template and the hybrid template features.

Object Visual Tracking

Lightweight Super-Resolution Head for Human Pose Estimation

1 code implementation31 Jul 2023 Haonan Wang, Jie Liu, Jie Tang, Gangshan Wu

We first propose the SR head, which predicts heatmaps with a spatial resolution higher than the input feature maps (or even consistent with the input image) by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing.

Pose Estimation Quantization +1

MaxSR: Image Super-Resolution Using Improved MaxViT

no code implementations14 Jul 2023 Bincheng Yang, Gangshan Wu

Because transformer models have powerful representation capacity and the in-built self-attention mechanisms in transformer models help to leverage self-similarity prior in input low-resolution image to improve performance for single image super-resolution, we present a single image super-resolution model based on recent hybrid vision transformer of MaxViT, named as MaxSR.

Image Super-Resolution

Transferring Foundation Models for Generalizable Robotic Manipulation

no code implementations9 Jun 2023 Jiange Yang, Wenhui Tan, Chuhao Jin, Keling Yao, Bei Liu, Jianlong Fu, Ruihua Song, Gangshan Wu, LiMin Wang

Improving the generalization capabilities of general-purpose robotic manipulation agents in the real world has long been a significant challenge.

Imitation Learning Object +1

MixFormerV2: Efficient Fully Transformer Tracking

1 code implementation NeurIPS 2023 Yutao Cui, Tianhui Song, Gangshan Wu, LiMin Wang

Our key design is to introduce four special prediction tokens and concatenate them with the tokens from target template and search areas.

Video Frame Interpolation with Densely Queried Bilateral Correlation

1 code implementation26 Apr 2023 Chang Zhou, Jie Liu, Jie Tang, Gangshan Wu

To better model correlations and to produce more accurate motion fields, we propose the Densely Queried Bilateral Correlation (DQBC) that gets rid of the receptive field dependency problem and thus is more friendly to small and fast-moving objects.

Motion Estimation Video Frame Interpolation

SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

1 code implementation ICCV 2023 Yutao Cui, Chenkai Zeng, Xiaoyu Zhao, Yichun Yang, Gangshan Wu, LiMin Wang

We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association.

Ranked #2 on Multiple Object Tracking on SportsMOT (using extra training data)

Multi-Object Tracking Multiple Object Tracking +1

LinK: Linear Kernel for LiDAR-based 3D Perception

1 code implementation CVPR 2023 Tao Lu, Xiang Ding, Haisong Liu, Gangshan Wu, LiMin Wang

Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from data scarcity and sparsity.

3D Object Detection 3D Semantic Segmentation +1

CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets

1 code implementation13 Feb 2023 Jiange Yang, Sheng Guo, Gangshan Wu, LiMin Wang

Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.

Contrastive Learning Representation Learning +1

MixFormer: End-to-End Tracking with Iterative Mixed Attention

1 code implementation6 Feb 2023 Yutao Cui, Cheng Jiang, Gangshan Wu, LiMin Wang

Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.

Visual Object Tracking

From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution

1 code implementation30 Nov 2022 Jie Liu, Chao Chen, Jie Tang, Gangshan Wu

In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a $3\times3$ convolution is applied to process the finest details.

Image Super-Resolution

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification

1 code implementation2 May 2022 Tao Lu, Chunxu Liu, Youxin Chen, Gangshan Wu, LiMin Wang

In the existing work, each point in the cloud may inevitably be selected as the neighbors of multiple aggregation centers, as all centers will gather neighbor features from the whole point cloud independently.

3D Classification 3D Point Cloud Classification +1

Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

1 code implementation18 Apr 2022 Zongcai Du, Ding Liu, Jie Liu, Jie Tang, Gangshan Wu, Lean Fu

Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution.

Image Super-Resolution

MixFormer: End-to-End Tracking with Iterative Mixed Attention

1 code implementation CVPR 2022 Yutao Cui, Cheng Jiang, LiMin Wang, Gangshan Wu

Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.

Semi-Supervised Video Object Segmentation Visual Object Tracking

Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection

no code implementations1 Mar 2022 Jing Tan, Yuhong Wang, Gangshan Wu, LiMin Wang

Instead, in this paper, we present Temporal Perceiver, a general architecture with Transformer, offering a unified solution to the detection of arbitrary generic boundaries, ranging from shot-level, event-level, to scene-level GBDs.

Avg Boundary Detection +1

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning

1 code implementation31 Dec 2021 Bin-Cheng Yang, Gangshan Wu

By introducing dual path connections inspired by Dual Path Networks into EMSRDPN, it uses residual connections and dense connections in an integrated way in most network layers.

Image Super-Resolution

AdaDM: Enabling Normalization for Image Super-Resolution

1 code implementation27 Nov 2021 Jie Liu, Jie Tang, Gangshan Wu

We found that the standard deviation of the residual feature shrinks a lot after normalization layers, which causes the performance degradation in SR networks.

Image Super-Resolution

A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark

1 code implementation24 Oct 2021 Zhenxi Zhu, LiMin Wang, Sheng Guo, Gangshan Wu

In this paper, we aim to present an in-depth study on few-shot video classification by making three contributions.

Classification Meta-Learning +2

Mutual Supervision for Dense Object Detection

no code implementations ICCV 2021 Ziteng Gao, LiMin Wang, Gangshan Wu

In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency.

Classification Dense Object Detection +3

Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

2 code implementations10 Sep 2021 Zhenzhi Wang, LiMin Wang, Tao Wu, TianHao Li, Gangshan Wu

Instead, from a perspective on temporal grounding as a metric-learning problem, we present a Mutual Matching Network (MMN), to directly model the similarity between language queries and video moments in a joint embedding space.

Metric Learning Representation Learning +2

Self Supervision to Distillation for Long-Tailed Visual Recognition

1 code implementation ICCV 2021 TianHao Li, LiMin Wang, Gangshan Wu

In this paper, we show that soft label can serve as a powerful solution to incorporate label correlation into a multi-stage training scheme for long-tailed recognition.

Long-tail Learning

Target Adaptive Context Aggregation for Video Scene Graph Generation

1 code implementation ICCV 2021 Yao Teng, LiMin Wang, Zhifeng Li, Gangshan Wu

Specifically, we design an efficient method for frame-level VidSGG, termed as {\em Target Adaptive Context Aggregation Network} (TRACE), with a focus on capturing spatio-temporal context information for relation recognition.

Graph Generation Relation +2

CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation

1 code implementation CVPR 2021 Tao Lu, LiMin Wang, Gangshan Wu

Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories.

Segmentation Semantic Segmentation

SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

1 code implementation6 Jun 2021 Zeyu Ruan, Changqing Zou, Longhai Wu, Gangshan Wu, LiMin Wang

Three-dimensional face dense alignment and reconstruction in the wild is a challenging problem as partial facial information is commonly missing in occluded and large pose face images.

3D Face Alignment 3D Face Reconstruction +3

Anchor-based Plain Net for Mobile Image Super-Resolution

3 code implementations20 May 2021 Zongcai Du, Jie Liu, Jie Tang, Gangshan Wu

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward.

Image Super-Resolution Quantization

MGSampler: An Explainable Sampling Strategy for Video Action Recognition

1 code implementation ICCV 2021 Yuan Zhi, Zhan Tong, LiMin Wang, Gangshan Wu

First, we present two different motion representations to enable us to efficiently distinguish the motion-salient frames from the background.

Action Recognition Temporal Action Localization

Target Transformed Regression for Accurate Tracking

1 code implementation1 Apr 2021 Yutao Cui, Cheng Jiang, LiMin Wang, Gangshan Wu

Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos.

regression Visual Object Tracking +1

Relaxed Transformer Decoders for Direct Action Proposal Generation

2 code implementations ICCV 2021 Jing Tan, Jiaqi Tang, LiMin Wang, Gangshan Wu

Extensive experiments on THUMOS14 and ActivityNet-1. 3 benchmarks demonstrate the effectiveness of RTD-Net, on both tasks of temporal action proposal generation and temporal action detection.

Action Detection Temporal Action Proposal Generation +1

Temporal Difference Networks for Action Recognition

no code implementations1 Jan 2021 LiMin Wang, Bin Ji, Zhan Tong, Gangshan Wu

To mitigate this issue, this paper presents a new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition.

Action Recognition In Videos

TDN: Temporal Difference Networks for Efficient Action Recognition

1 code implementation CVPR 2021 LiMin Wang, Zhan Tong, Bin Ji, Gangshan Wu

To mitigate this issue, this paper presents a new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition.

Action Classification Action Recognition In Videos

Residual Feature Distillation Network for Lightweight Image Super-Resolution

2 code implementations24 Sep 2020 Jie Liu, Jie Tang, Gangshan Wu

Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN).

Image Super-Resolution

Context-Aware RCNN: A Baseline for Action Detection in Videos

3 code implementations ECCV 2020 Jianchao Wu, Zhanghui Kuang, Li-Min Wang, Wayne Zhang, Gangshan Wu

In this work, we first empirically find the recognition accuracy is highly correlated with the bounding box size of an actor, and thus higher resolution of actors contributes to better performance.

Action Detection Action Recognition

Fully Convolutional Online Tracking

2 code implementations15 Apr 2020 Yutao Cui, Cheng Jiang, Li-Min Wang, Gangshan Wu

To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm.

Real-Time Visual Tracking regression

Actions as Moving Points

2 code implementations ECCV 2020 Yixuan Li, Zixu Wang, Li-Min Wang, Gangshan Wu

The existing action tubelet detectors often depend on heuristic anchor design and placement, which might be computationally expensive and sub-optimal for precise localization.

Action Detection Action Recognition

Simple and Lightweight Human Pose Estimation

1 code implementation23 Nov 2019 Zhe Zhang, Jie Tang, Gangshan Wu

Specifically, our LPN-50 can achieve 68. 7 in AP score on the COCO test-dev set, with only 2. 7M parameters and 1. 0 GFLOPs, while the inference speed is 17 FPS on an Intel i7-8700K CPU machine.

Keypoint Detection Novel Concepts +1

LIP: Local Importance-based Pooling

1 code implementation ICCV 2019 Ziteng Gao, Li-Min Wang, Gangshan Wu

Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption.

Image Classification Object Detection

Dynamically Visual Disambiguation of Keyword-based Image Search

no code implementations27 May 2019 Yazhou Yao, Zeren Sun, Fumin Shen, Li Liu, Li-Min Wang, Fan Zhu, Lizhong Ding, Gangshan Wu, Ling Shao

To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.

General Classification Image Retrieval

Translate-to-Recognize Networks for RGB-D Scene Recognition

1 code implementation CVPR 2019 Dapeng Du, Li-Min Wang, Huiling Wang, Kai Zhao, Gangshan Wu

Empirically, we verify that this new semi-supervised setting is able to further enhance the performance of recognition network.

Scene Recognition Translation

Learning Actor Relation Graphs for Group Activity Recognition

2 code implementations CVPR 2019 Jianchao Wu, Li-Min Wang, Li Wang, Jie Guo, Gangshan Wu

To this end, we propose to build a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture the appearance and position relation between actors.

Action Recognition Group Activity Recognition +1

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