36 citations · 188 across the 36 of their papers we have counts for
37 papers
Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series
Li Zhang, Nital Patel, Xiuqi Li +1
Time series chain (TSC) is a recently introduced concept that captures the evolving patterns in large scale time series. Informally, a time series chain is a temporally ordered set…
Frequency-Assisted Adaptive Sharpening Scheme Considering Bitrate and Quality Tradeoff
Yingxue Pang, Shijie Zhao, Haiqiang Wang +3
Sharpening is a widely adopted technique to improve video quality, which can effectively emphasize textures and alleviate blurring. However, increasing the sharpening level comes w…
Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting
Xian Lin, Yangyang Xiang, Li Yu +1
End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization. With recent brea…
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Zhihao Li, Jiancheng Yang, Yongchao Xu +3
Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based…
Video Compression with Arbitrary Rescaling Network
Mengxi Guo, Shijie Zhao, Hao Jiang +2
Most video platforms provide video streaming services with different qualities, and the quality of the services is usually adjusted by the resolution of the videos. So high-resolut…
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
Zifan Chen, Jiazheng Li, Jie Zhao +5
**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors,…
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
Ming Cheng, Haoyu Ma, Qiufang Ma +7
Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, th…
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution
Xiaopeng Sun, Weiqi Li, Zhenyu Zhang +8
360° omnidirectional images have gained research attention due to their immersive and interactive experience, particularly in AR/VR applications. However, they suffer from lower an…
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
Junge Zhang, Feihu Zhang, Shaochen Kuang +1
Labeling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training…
Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling
Jinhai Yang, Mengxi Guo, Shijie Zhao +2
High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work i…
Single-view Neural Radiance Fields with Depth Teacher
Yurui Chen, Chun Gu, Feihu Zhang +1
Neural Radiance Fields (NeRF) have been proposed for photorealistic novel view rendering. However, it requires many different views of one scene for training. Moreover, it has poor…
S-NeRF: Neural Radiance Fields for Street Views
Ziyang Xie, Junge Zhang, Wenye Li +2
Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this parad…
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
Jiaxiang Dong, Haixu Wu, Haoran Zhang +3
Time series analysis is widely used in extensive areas. Recently, to reduce labeling expenses and benefit various tasks, self-supervised pre-training has attracted immense interest…
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series
Li Zhang, Jiahao Ding, Yifeng Gao +1
Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven servi…
LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification
Wenjie Xi, Arnav Jain, Li Zhang +1
Time series classification is an important data mining task that has received a lot of interest in the past two decades. Due to the label scarcity in practice, semi-supervised time…
Robust Time Series Chain Discovery with Incremental Nearest Neighbors
Li Zhang, Yan Zhu, Yifeng Gao +1
Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of tim…
Data-Driven Deep Supervision for Skin Lesion Classification
Suraj Mishra, Yizhe Zhang, Li Zhang +3
Automatic classification of pigmented, non-pigmented, and depigmented non-melanocytic skin lesions have garnered lots of attention in recent years. However, imaging variations in s…
Device-centric Federated Analytics At Ease
Li Zhang, Junji Qiu, Shangguang Wang +1
Nowadays, high-volume and privacy-sensitive data are generated by mobile devices, which are better to be preserved on devices and queried on demand. However, data analysts still la…
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Hexin Dong, Zifan Chen, Mingze Yuan +5
As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally…
In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking
Yao Sui, Guanghui Wang, Li Zhang
The paper focuses on a classical tracking model, subspace learning, grounded on the fact that the targets in successive frames are considered to reside in a low-dimensional subspac…
Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
Ning Ge, Guanghao Li, Li Zhang +1
Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning…
Few-shot Action Recognition with Prototype-centered Attentive Learning
Xiatian Zhu, Antoine Toisoul, Juan-Manuel Perez-Rua +3
Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode…
Direct Classification of Emotional Intensity
Jacob Ouyang, Isaac R Galatzer-Levy, Vidya Koesmahargyo +1
In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units. Using a 3d DNN incorporated with dynam…
LID 2020: The Learning from Imperfect Data Challenge Results
Yunchao Wei, Shuai Zheng, Ming-Ming Cheng +32
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotate…