Temporal Recurrent Networks for Online Action Detection

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Network (TRN), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art

Influencing factors on green supply chain resilience of agricultural products: an improved gray-DEMATEL-ISM approach

IntroductionNatural disasters and the COVID-19 epidemic have caused serious consequences such as long-term disruption and chain reaction to the global supply chain. Global warming caused by a large number of greenhouse gases has accelerated the attention of society to environmental sustainability. Therefore, identifying the transmission path of the factors that affect the green supply chain resilience of agricultural products is the primary task to accelerate the construction of a modern circulation system of agricultural products, ensure market supply and protect the environment.MethodsBased on the stakeholder theory, this study uses the literature research method to identify 15 factors that affect the green supply chain resilience of agricultural products. Through improving DEMATEL and ISM to study the internal relationship between the influencing factors, build a multi-level hierarchical structure model, and identify the basic transmission process and path of the influencing factors.ResultsThe results show that the government’s issuance of environmental policies and the provision of financial subsidies are important driving forces to strengthen the green supply chain resilience of agricultural products; The collaboration capability and business sustainability goals directly affect the green supply chain resilience of agricultural products; Agility, digital infrastructure construction, sustainability beliefs of top managers, public opinion with environment information disclosure and other factors indirectly affect the green supply chain resilience of agricultural products.DiscussionThe conclusion shows that the most important way to guide the green supply chain of agricultural products to develop towards standardization, normalization and sustainability is to guide the organization to set business sustainable goals and strengthen the collaborative cooperation ability of all stakeholders in the supply chain, with the government issuing environmental policies and providing financial subsidies as the driving factors. This study can provide theoretical basis for the government and enterprises to strengthen the green supply chain resilience of agricultural products

A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent support prototypes and segment the query image by feeding the mixed features to a decoder. Although significant improvements have been achieved, existing methods are still face class biases due to class variants and background confusion. In this paper, we propose a joint framework that combines more valuable class-aware and class-agnostic alignment guidance to facilitate the segmentation. Specifically, we design a hybrid alignment module which establishes multi-scale query-support correspondences to mine the most relevant class-aware information for each query image from the corresponding support features. In addition, we explore utilizing base-classes knowledge to generate class-agnostic prior mask which makes a distinction between real background and foreground by highlighting all object regions, especially those of unseen classes. By jointly aggregating class-aware and class-agnostic alignment guidance, better segmentation performances are obtained on query images. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ datasets demonstrate that our proposed joint framework performs better, especially on the 1-shot setting