Search Results for author: Gangshan Wu

Found 26 papers, 18 papers with code

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

no code implementations24 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

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

no 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 Dual Matching Network (DMN), to directly model the relations between language queries and video moments in a joint embedding space.

Metric Learning Representation Learning

Self Supervision to Distillation for Long-Tailed Visual Recognition

no code implementations 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.

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 Scene Graph Generation

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.

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 +2

Anchor-based Plain Net for Mobile Image Super-Resolution

1 code implementation20 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

2 code implementations 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

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.

Visual Object Tracking Visual Tracking

Relaxed Transformer Decoders for Direct Action Proposal Generation

1 code implementation 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 Action Recognition In Videos +1

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.

Ranked #5 on Action Recognition on Something-Something V2 (using extra training data)

Action Classification Action Recognition +2

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

1 code implementation 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

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

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

no code implementations 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

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