Two-Stream Action Recognition-Oriented Video Super-Resolution

We study the video super-resolution (SR) problem for facilitating video analytics tasks, e.g. action recognition, instead of for visual quality. The popular action recognition methods based on convolutional networks, exemplified by two-stream networks, are not directly applicable on video of low spatial resolution. This can be remedied by performing video SR prior to recognition, which motivates us to improve the SR procedure for recognition accuracy. Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively. On the one hand, we observe that regions with action are more important to recognition, and we propose an optical-flow guided weighted mean-squared-error loss for our spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving objects. On the other hand, we observe that existing video SR methods incur temporal discontinuity between frames, which also worsens the recognition accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR) training that emphasizes the temporal continuity between consecutive frames. We perform experiments using two state-of-the-art action recognition networks and two well-known datasets--UCF101 and HMDB51. Results demonstrate the effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.Comment: Accepted to ICCV 2019. Code: https://github.com/AlanZhang1995/TwoStreamS

Investigation of Subjectively Assessed Health Symptoms and Human Thermal Perceptions in Transient Thermal Environments

AbstractPeople are often likely to expose themselves to sudden temperature change in daily life and they may suffer from not only thermal discomfort but also even some health symptoms. In this study, the influence of different air temperature steps (S5:32°C-37°C-32°C, S11:26°C-37°C-26°C, and S15:22°C-37°C-22°C) on subjective health symptoms and thermal perceptions was studied with 24 volunteered participants in the laboratory experiment. Several subjective rating scales were used to assess participant's subjective feelings imposed by temperature steps. Our results show that perspiration, eyestrain, dizziness, accelerated respiration and heart rate are found to be sensitive self-reported symptoms in response to temperature step changes. Thermal sensation and comfort just before temperature step are significantly distinguished from that immediately after step change except for thermal comfort under up step situation of S15 (22oC-37oC). Moreover, temperature step amplitude and direction have significant impact on subjective perceptions

Research on Online Discourse of Cross-Border E-commerce Platform on the Basis of Co-operative Principle

China’s cross-border e-commerce industry has shown a blow-out trend fueled by the rapidly-improved informatization of the society and powerful support of government. However, the increasing platforms in the market, the homogenizing merchandise, and the decreasing of conversion cost bring enormous challenges to the platforms. In order to survive in the fierce competition, they must improve the quality of services in the communication with consumers so as to enhance the core competitiveness centered on consumers. In this research, we compiled 100 real cases of cross-border e-commerce platform online discourse by collecting chat records from the online interaction between 50 college students and the platform merchants on the Tmall international platform. This study analyzes these online discourse cases from the perspective of Grice’s Co-operative principle. The study demonstrates that, the application of Grice’s Co-operative principle can enable cross-border e-commerce platforms to effectively adopt and employ the most appropriate interactive discourse, which exerts an tremendous influence on improving the quality of communication services. And there is a positive correlation between the usage of the Co-operative principle and the monthly sales volume of the platform

Domain Adaptive Synapse Detection with Weak Point Annotations

The development of learning-based methods has greatly improved the detection of synapses from electron microscopy (EM) images. However, training a model for each dataset is time-consuming and requires extensive annotations. Additionally, it is difficult to apply a learned model to data from different brain regions due to variations in data distributions. In this paper, we present AdaSyn, a two-stage segmentation-based framework for domain adaptive synapse detection with weak point annotations. In the first stage, we address the detection problem by utilizing a segmentation-based pipeline to obtain synaptic instance masks. In the second stage, we improve model generalizability on target data by regenerating square masks to get high-quality pseudo labels. Benefiting from our high-accuracy detection results, we introduce the distance nearest principle to match paired pre-synapses and post-synapses. In the WASPSYN challenge at ISBI 2023, our method ranks the 1st place