Photocatalytic selective oxidation of glycerol to formic acid and formaldehyde over surface cobalt-doped titanium dioxide.

Glycerol is one of the most important biomass platform compounds that is a by-product of biodiesel production, and the selective cleavage of the CC bond of glycerol to produce liquid hydrogen carriers (i.e., formic acid and formaldehyde) offers a viable strategy to alleviate the currently faced energy shortages.

Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling.

Due to the inherent unorderliness and irregularity of point cloud, points emerge inconsistently across different frames in a point cloud video. To capture the dynamics in point cloud videos, tracking points and limiting temporal modeling range are usually employed to preserve spatio-temporal structure. However, as points may flow in and out across frames, computing accurate point trajectories is extremely difficult, especially for long videos.

Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition.

In point cloud videos, point coordinates are irregular and unordered but point timestamps exhibit regularities and order. Grid-based networks for conventional video processing cannot be directly used to model raw point cloud videos. Therefore, in this work, we propose a point-based network that directly handles raw point cloud videos.

Few-Shot Common-Object Reasoning Using Common-Centric Localization Network.

In the few-shot common-localization task, given few support images without bounding box annotations at each episode, the goal is to localize the common object in the query image of unseen categories. The few-shot common-localization task involves common object reasoning from the given images, predicting the spatial locations of the object with different shapes, sizes, and orientations. In this work, we propose a common-centric localization (CCL) network for few-shot common-localization.

Unsupervised Visual Representation Learning via Dual-Level Progressive Similar Instance Selection.

The superiority of deeply learned representations relies on large-scale labeled datasets. However, annotating data are usually expensive or even infeasible in some scenarios. To address this problem, we propose an unsupervised method to leverage instance discrimination and similarity for deep visual representation learning.