547 citations · 697 across the 15 of their papers we have counts for
15 papers
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Yi-Fan Zhang, Qingsong Wen, Xue Wang +6
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms a…
Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark
Andong Lu, Zhang Zhang, Yan Huang +4
Nighttime person Re-ID (person re-identification in the nighttime) is a very important and challenging task for visual surveillance but it has not been thoroughly investigated. Und…
Leveraging Artificial Intelligence on Binary Code Comprehension
Yifan Zhang
Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary cod…
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
Weihan Cao, Yifan Zhang, Jianfei Gao +3
Knowledge distillation(KD) is a widely-used technique to train compact models in object detection. However, there is still a lack of study on how to distill between heterogeneous d…
Learnable Locality-Sensitive Hashing for Video Anomaly Detection
Yue Lu, Congqi Cao, Yanning Zhang
Video anomaly detection (VAD) mainly refers to identifying anomalous events that have not occurred in the training set where only normal samples are available. Existing works usual…
Efficient Spatialtemporal Context Modeling for Action Recognition
Congqi Cao, Yue Lu, Yifan Zhang +2
Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. How…
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there…
StablePose: Learning 6D Object Poses from Geometrically Stable Patches
Yifei Shi, Junwen Huang, Xin Xu +2
We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from…
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and env…
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
Bo Pang, Yizhuo Li, Yifan Zhang +2
Multi-object tracking is a fundamental vision problem that has been studied for a long time. As deep learning brings excellent performances to object detection algorithms, Tracking…
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
Skeleton data, which consists of only the 2D/3D coordinates of the human joints, has been widely studied for human action recognition. Existing methods take the semantics as prior…
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
Lei Shi, Yifan Zhang, Jian Cheng +1
Graph convolutional networks (GCNs), which generalize CNNs to more generic non-Euclidean structures, have achieved remarkable performance for skeleton-based action recognition. How…
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision
Lei Shi, Yifan Zhang, Jian Cheng +1
Pose-based action recognition has drawn considerable attention recently. Existing methods exploit the joint positions to extract the body-part features from the activation map of t…
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Lei Shi, Yifan Zhang, Jian Cheng +1
In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. Howe…
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition
Congqi Cao, Yifan Zhang, Chunjie Zhang +1
Three dimensional convolutional neural networks (3D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which…