Visual Boundary-Guided Pseudo-Labeling for Weakly Supervised 3D Point Cloud Segmentation in Indoor Environments.

Accurate segmentation of 3D point clouds in indoor scenes remains a challenging task, often hindered by the labor-intensive nature of data annotation. While weakly supervised learning approaches have shown promise in leveraging partial annotations, they frequently struggle with imbalanced performance between foreground and background elements due to the complex structures and proximity of objects in indoor environments. To address this issue, we propose a novel foreground-aware label enhancement method utilizing visual boundary priors.

Multistage Spatio-Temporal Networks for Robust Sketch Recognition.

Sketch recognition relies on two types of information, namely, spatial contexts like the local structures in images and temporal contexts like the orders of strokes. Existing methods usually adopt convolutional neural networks (CNNs) to model spatial contexts, and recurrent neural networks (RNNs) for temporal contexts. However, most of them combine spatial and temporal features with late fusion or single-stage transformation, which is prone to losing the informative details in sketches.

Joint Feature Optimization and Fusion for Compressed Action Recognition.

Recent methods including CoViAR and DMC-Net provide a new paradigm for action recognition since they are directly targeted at compressed videos (e.g., MPEG4 files).