Search Results for author: Ya-Li Li

Found 14 papers, 5 papers with code

Do Different Tracking Tasks Require Different Appearance Models?

1 code implementation5 Jul 2021 Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto

We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain performance that is competitive against specialised methods for all the five tasks considered.

Multi-Object Tracking Multi-Object Tracking and Segmentation +10

Video Super-resolution with Temporal Group Attention

1 code implementation CVPR 2020 Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, Qi Tian

Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention.

Video Super-Resolution

CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

no code implementations ECCV 2020 Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Ya-Li Li, Shengjin Wang

This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.

Multi-Object Tracking Person Re-Identification +1

Towards Real-Time Multi-Object Tracking

10 code implementations ECCV 2020 Zhongdao Wang, Liang Zheng, Yixuan Liu, Ya-Li Li, Shengjin Wang

In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.

Ranked #9 on Multi-Object Tracking on MOT16 (using extra training data)

Multiple Object Tracking Multi-Task Learning +1

Softmax Dissection: Towards Understanding Intra- and Inter-class Objective for Embedding Learning

no code implementations4 Aug 2019 Lanqing He, Zhongdao Wang, Ya-Li Li, Shengjin Wang

The softmax loss and its variants are widely used as objectives for embedding learning, especially in applications like face recognition.

Face Recognition Face Verification

CS-R-FCN: Cross-supervised Learning for Large-Scale Object Detection

no code implementations30 May 2019 Ye Guo, Ya-Li Li, Shengjin Wang

Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale object detection for thousands of categories.

Object Detection

Intra-clip Aggregation for Video Person Re-identification

no code implementations5 May 2019 Takashi Isobe, Jian Han, Fang Zhu, Ya-Li Li, Shengjin Wang

Video-based person re-identification has drawn massive attention in recent years due to its extensive applications in video surveillance.

Data Augmentation Video-Based Person Re-Identification

HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection

no code implementations25 Apr 2019 Ya-Li Li, Shengjin Wang

First, we present the modules of spatial attention, channel attention and aligned attention for single-stage object detection.

Object Detection

Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification

no code implementations CVPR 2019 Yifan Sun, Qin Xu, Ya-Li Li, Chi Zhang, Yikang Li, Shengjin Wang, Jian Sun

The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images).

Person Re-Identification

Linkage Based Face Clustering via Graph Convolution Network

3 code implementations CVPR 2019 Zhongdao Wang, Liang Zheng, Ya-Li Li, Shengjin Wang

The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.

Face Clustering Link Prediction

Intention Oriented Image Captions with Guiding Objects

no code implementations CVPR 2019 Yue Zheng, Ya-Li Li, Shengjin Wang

In this paper, we propose a novel approach for generating image captions with guiding objects (CGO).

Image Captioning

DeepDeblur: Fast one-step blurry face images restoration

no code implementations27 Nov 2017 Lingxiao Wang, Ya-Li Li, Shengjin Wang

Comprehensive experiments demonstrate that our proposed method can handle various blur kenels and achieve state-of-the-art results for small size blurry face images restoration.

Deblurring Face Recognition

Progressive Representation Adaptation for Weakly Supervised Object Localization

1 code implementation12 Oct 2017 Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang

In classification adaptation, we transfer a pre-trained network to a multi-label classification task for recognizing the presence of a certain object in an image.

Classification General Classification +3

Weakly Supervised Object Localization With Progressive Domain Adaptation

no code implementations CVPR 2016 Dong Li, Jia-Bin Huang, Ya-Li Li, Shengjin Wang, Ming-Hsuan Yang

In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation.

Classification Domain Adaptation +4

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