13 accepted papers
Image-based feature representation plays a critical role in visual localization, enabling robots to estimate their position and orientation in GPS-denied environments. However, this task is often undermined by significant variations in camera viewpoints and scene appearances. Recently, map-free visu
Visual anomaly detection is essential and commonly used for many tasks in the field of robotic vision. Recent anomaly detection datasets mainly focus on industrial automated inspection, medical image analysis and video surveillance. With the development of unmanned supermarkets, anomaly detection pl
Unsupervised visual anomaly detection uses only anomaly-free images to detect anomalous patterns, whose recent methods mainly focus on the anomaly classification sub-task but neglect to localize anomalies accurately. Existing reconstruction-based and representation-based methods yield anomaly score
Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging. Existing video-based methods generally recover human mesh by estimating the complex pose and shape parameters from coupled image features, w…
3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertex-vertex relations, which often le…
3D human pose estimation errors would propagate along the human body topology and accumulate at the end joints of limbs. Inspired by the backtracking mechanism in automatic control systems, we design an Intra-Part Constraint module that utilizes the parent nodes as the reference to build topological…
Despite substantial progress in 3D human pose estimation from a single-view image, prior works rarely explore global and local correlations, leading to insufficient learning of human skeleton representations. To address this issue, we propose a novel Interweaved Graph and Attention Network (IGANet)…
Facial expression recognition from a single image has potential applications in fields including human-computer interaction and medical diagnosis. Most recent methods use deep neural networks to directly learn from a roughly cropped facial image which is usually detected from a whole image by face d…
Monocular self-supervised depth estimation can be easily applied in many areas since only a single camera is required. However, current methods do not predict well in depth borders. Besides, factors such as occlusion and texture sparsity can lead to the failure of the photometric consistency, affect…
In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing contrastive learning methods use normal augmentations to construct similar positive samples, which limits the ability to ex…
Surface defect detection is a common task for industrial quality control, which increasingly requires accuracy and real-time ability. However, the current segmentation networks are not effective in dealing with defect boundary details, local similarity of different defects and low contrast between d…
Defect detection has been widely studied in computer vision and used in industrial production. However, most existing methods for defect detection mainly suffer three drawbacks: i) Low-contrast problem between defects and background. ii) Large scale changes in defects size. iii) Extreme imbalance pr…
Human action recognition based on skeleton sequences has wide applications in human-computer interaction and intelligent surveillance. Although previous methods have successfully applied Long Short-Term Memory(LSTM) networks to model shape evolution of human actions, it still remains a problem to ef…