1 code implementation • ECCV 2022 2022 • Jhih-Ciang Wu*, He-Yen Hsieh*, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu
Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence.
Anomaly Detection In Surveillance Videos Self-Supervised Learning
no code implementations • 29 Sep 2021 • Bing-Jhang Lin, Ding-Jie Chen, He-Yen Hsieh, Tyng-Luh Liu
We comprehensively identify the missing neighborhood relationships issue of conventional embedding and propose a novel approach, termed as Graph Local Embedding (GLE), to deep metric learning.
no code implementations • CVPR 2021 • Ding-Jie Chen, He-Yen Hsieh, Tyng-Luh Liu
One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch.
no code implementations • 1 Jan 2021 • He-Yen Hsieh, Ding-Jie Chen, Tung-Ying Lee, Tyng-Luh Liu
The task of temporal action proposal generation (TAPG) aims to provide high-quality video segments, i. e., proposals that potentially contain action events.
no code implementations • 1 Jan 2021 • Yen-Chi Hsu, Cheng-Yao Hong, Wan-Cyuan Fan, Ding-Jie Chen, Ming-Sui Lee, Davi Geiger, Tyng-Luh Liu
The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task.
no code implementations • ICCV 2021 • Jhih-Ciang Wu, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu
Anomaly detection (AD) aims to address the task of classification or localization of image anomalies.
no code implementations • None 2019 • Yen-Chi Hsu, Cheng-Yao Hong, Ding-Jie Chen, Ming-Sui Lee, Davi Geiger, Tyng-Luh Liu
We introduce a regularization concept based on the proposed Batch Confusion Norm (BCN) to address Fine-Grained Visual Classification (FGVC).
Ranked #17 on Fine-Grained Image Classification on FGVC Aircraft
no code implementations • ICCV 2019 • Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, Tyng-Luh Liu
With the refined heatmap, we update the textual representation of the referring expression by re-evaluating its attention distribution and then compute a new STEP heatmap as the next input to the ConvRNN.
Ranked #16 on Referring Expression Segmentation on RefCOCO testB
1 code implementation • CVPR 2019 • Songhao Jia, Ding-Jie Chen, Hwann-Tzong Chen
This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem.
3 code implementations • 20 Dec 2018 • Ding-Jie Chen, Jui-Ting Chien, Hwann-Tzong Chen, Tyng-Luh Liu
This paper presents a "learning to learn" approach to figure-ground image segmentation.
no code implementations • 18 Dec 2018 • Ding-Jie Chen, Hwann-Tzong Chen, Long-Wen Chang
At each round of interaction the user is only presented with a small number of informative query seeds that are far apart from each other.