RESUMO
Effective spatio-temporal modeling as a core of video representation learning is challenged by complex scale variations in spatio-temporal cues in videos, especially different visual tempos of actions and varying spatial sizes of moving objects. Most of the existing works handle complex spatio-temporal scale variations based on input-level or feature-level pyramid mechanisms, which, however, rely on expensive multistream architectures or explore multiscale spatio-temporal features in a fixed manner. To effectively capture complex scale dynamics of spatio-temporal cues in an efficient way, this article proposes a single-stream architecture (SS-Arch.) with single-input namely, adaptive multi-granularity spatio-temporal network (AMS-Net) to model adaptive multi-granularity (Multi-Gran.) Spatio-temporal cues for video action recognition. To this end, our AMS-Net proposes two core components, namely, competitive progressive temporal modeling (CPTM) block and collaborative spatio-temporal pyramid (CSTP) module. They, respectively, capture fine-grained temporal cues and fuse coarse-level spatio-temporal features in an adaptive manner. It admits that AMS-Net can handle subtle variations in visual tempos and fair-sized spatio-temporal dynamics in a unified architecture. Note that our AMS-Net can be flexibly instantiated based on existing deep convolutional neural networks (CNNs) with the proposed CPTM block and CSTP module. The experiments are conducted on eight video benchmarks, and the results show our AMS-Net establishes state-of-the-art (SOTA) performance on fine-grained action recognition (i.e., Diving48 and FineGym), while performing very competitively on widely used Something-Something and Kinetics.
RESUMO
Environmental allergens elicit complex immune responses in the lungs that can promote the development of asthma or exacerbate preexisting asthma in susceptible individuals. House dust mites are one of the most common indoor allergens and are a significant driver of allergic disease. Respiratory infections are known factors in acute exacerbations of asthma but the impact of allergen on the pathogen is not well understood. We investigated the pathogenesis of influenza A infection following exposure to house dust mites. Mice exposed to house dust mites lose less weight following infection and had more transcription of interferon-lambda than controls. These data correlated with less transcription of the influenza polymerase acidic gene suggesting diminished viral replication in house dust mite exposed mice. Altogether, these data suggest that exposure to environmental allergens can influence the pathogenesis of influenza infection.
