Exploring Temporal Preservation Networks for Precise Temporal Action Localization

Temporal action localization is an important task of computer vision. Though a variety of methods have been proposed, it still remains an open question how to predict the temporal boundaries of action segments precisely. Most works use segment-level classifiers to select video segments pre-determined by action proposal or dense sliding windows. However, in order to achieve more precise action boundaries, a temporal localization system should make dense predictions at a fine granularity. A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization. However, CDC network loses temporal information partially due to the temporal downsampling operation. In this paper, we propose an elegant and powerful Temporal Preservation Convolutional (TPC) Network that equips 3D ConvNets with TPC filters. TPC network can fully preserve temporal resolution and downsample the spatial resolution simultaneously, enabling frame-level granularity action localization. TPC network can be trained in an end-to-end manner. Experiment results on public datasets show that TPC network achieves significant improvement on per-frame action prediction and competing results on segment-level temporal action localization

The effect of addition of Nd and Ce on the microstructure and mechanical properties of ZM21 Mg alloy

AbstractThe microstructures and mechanical properties of Mg–2.0Zn–1.0Mn (ZM21) alloys with certain amount of Ce and Nd additions were investigated, and the influence mechanism of Ce and Nd on the microstructures and mechanical properties of extruded alloys was discussed. The results indicated that the addition of Nd and Ce can refine the grains in ZM21 alloy, for which the distribution density of second phase particle played a major role to hinder the growth of dynamic recrystallization (DRX) grain in alloys by adding a content of 0.4 wt.% Ce and Nd. The average grain size of ZM21 alloy with the additions of 0.4 wt.% Nd and Ce reached 6 ± 3 μm and 13 ± 2 μm, respectively. Adding Ce and Nd to ZM21 alloy, the changes of mechanical properties were mainly attributed to a reduction in basal texture intensity, refinement grain size as well as the dispersion density and distribution position of fine second phase particles. Furthermore, by addition of Ce and Nd to ZM21 alloy, the non-basal plane slip system could be activated which decreased the basal texture intensity

Study of a Flexible UAV Proprotor

This paper is concerned with the evaluation of design techniques, both for the propulsive performance and for the structural behavior of a composite flexible proprotor. A numerical model was developed using a combination of aerodynamic model based on Blade Element Momentum Theory (BEMT), and structural model based on anisotropic beam finite element, in order to evaluate the coupled structural and the aerodynamic characteristics of the deformable proprotor blade. The numerical model was then validated by means of static performance measurements and shape reconstruction from Laser Distance Sensor (LDS) outputs. From the validation results of both aerodynamic and structural model, it can be concluded that the numerical approach developed by the authors is valid as a reliable tool for designing and analyzing the UAV-sized proprotor made of composite material. The proposed experiment technique is also capable of providing a predictive and reliable data in blade geometry and performance for rotor modes

Image Clustering with External Guidance

The core of clustering is incorporating prior knowledge to construct supervision signals. From classic k-means based on data compactness to recent contrastive clustering guided by self-supervision, the evolution of clustering methods intrinsically corresponds to the progression of supervision signals. At present, substantial efforts have been devoted to mining internal supervision signals from data. Nevertheless, the abundant external knowledge such as semantic descriptions, which naturally conduces to clustering, is regrettably overlooked. In this work, we propose leveraging external knowledge as a new supervision signal to guide clustering, even though it seems irrelevant to the given data. To implement and validate our idea, we design an externally guided clustering method (Text-Aided Clustering, TAC), which leverages the textual semantics of WordNet to facilitate image clustering. Specifically, TAC first selects and retrieves WordNet nouns that best distinguish images to enhance the feature discriminability. Then, to improve image clustering performance, TAC collaborates text and image modalities by mutually distilling cross-modal neighborhood information. Experiments demonstrate that TAC achieves state-of-the-art performance on five widely used and three more challenging image clustering benchmarks, including the full ImageNet-1K dataset