23 citations · 51 across the 15 of their papers we have counts for
10 papers · 1 filter
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
Teng Hu, Ran Yi, Haokun Zhu +5
Stroke-based rendering aims to recreate an image with a set of strokes. Most existing methods render complex images using an uniform-block-dividing strategy, which leads to boundar…
Dual Path Transformer with Partition Attention
Zhengkai Jiang, Liang Liu, Jiangning Zhang +3
This paper introduces a novel attention mechanism, called dual attention, which is both efficient and effective. The dual attention mechanism consists of two parallel components: l…
Long-Term Value of Exploration: Measurements, Findings and Algorithms
Yi Su, Xiangyu Wang, Elaine Ya Le +12
Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging.…
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection
Liang Liu, Boshen Zhang, Jiangning Zhang +6
Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particu…
Calibrated Teacher for Sparsely Annotated Object Detection
Haohan Wang, Liang Liu, Boshen Zhang +6
Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidab…
Spacecraft Anomaly Detection with Attention Temporal Convolution Network
Liang Liu, Ling Tian, Zhao Kang +1
Spacecraft faces various situations when carrying out exploration missions in complex space, thus monitoring the anomaly status of spacecraft is crucial to the development of \text…
Learning from Noisy Labels with Decoupled Meta Label Purifier
Yuanpeng Tu, Boshen Zhang, Yuxi Li +5
Training deep neural networks(DNN) with noisy labels is challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-lear…
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Yuanpeng Tu, Boshen Zhang, Yuxi Li +6
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithm…
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes
Yuanpeng Tu, Yuxi Li, Boshen Zhang +4
Robust autonomous driving requires agents to accurately identify unexpected areas (anomalies) in urban scenes. To this end, some critical issues remain open: how to design advisabl…
Rethinking Mobile Block for Efficient Attention-based Models
Jiangning Zhang, Xiangtai Li, Jian Li +7
This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) ser…