Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.Comment: CVPR 201

MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training

We propose MinVIS, a minimal video instance segmentation (VIS) framework that achieves state-of-the-art VIS performance with neither video-based architectures nor training procedures. By only training a query-based image instance segmentation model, MinVIS outperforms the previous best result on the challenging Occluded VIS dataset by over 10% AP. Since MinVIS treats frames in training videos as independent images, we can drastically sub-sample the annotated frames in training videos without any modifications. With only 1% of labeled frames, MinVIS outperforms or is comparable to fully-supervised state-of-the-art approaches on YouTube-VIS 2019/2021. Our key observation is that queries trained to be discriminative between intra-frame object instances are temporally consistent and can be used to track instances without any manually designed heuristics. MinVIS thus has the following inference pipeline: we first apply the trained query-based image instance segmentation to video frames independently. The segmented instances are then tracked by bipartite matching of the corresponding queries. This inference is done in an online fashion and does not need to process the whole video at once. MinVIS thus has the practical advantages of reducing both the labeling costs and the memory requirements, while not sacrificing the VIS performance. Code is available at: https://github.com/NVlabs/MinVI

Understanding Cross National Difference in Knowledge Seeking Behavior Model: A Survival Perspective

Electronic Knowledge Repository (EKR) is one of the most commonly deployed knowledge management technologies, yet its success is hindered by employees’ underutilization and further complicated when implemented in the multinational context. To address these challenges, we propose a research model by conceptualizing employees’ knowledge seeking via EKR as a survival-centric behavior, identifying the technology acceptance model as the individual-level explanation for EKR use, and drawing on the thermal demands-resources theory for explaining cross national behavioral differences. Using hierarchical linear modeling, we tested the model with data from 1352 randomly sampled knowledge workers across 30 nations. The results reveal interesting cross national behavioral patterns. Specifically, thermal climates and national wealth at the macro-level interactively moderate individual-level relationships between perceived ease of use and perceived usefulness and between perceived usefulness and behavioral intention

Cross-National Differences in Individual Knowledge-Seeking Patterns: A Climato-Economic Contextualization

Electronic Knowledge Repository (EKR) is one of the most commonly deployed knowledge management technologies, yet its success hinges upon employees’ continued use and is further complicated in today’s multinational context. We integrate multiple theoretical linkages into a research model, conceptualizing knowledge-seeking as an instrumental behavior, adopting the technology acceptance model to characterize the individual-level continued EKR knowledge-seeking behavioral model, and drawing on the climato-economic theory to explain cross-national behavioral differences. Using hierarchical linear modeling (HLM), we test the model with data from 1352 randomly sampled knowledge workers across 30 nations. We find that two national-level factors, climate harshness and national wealth, interactively moderate the individual-level relationship between perceived usefulness (PU) and behavioral intention (BI) to continue seeking knowledge from EKR, such that the difference in the strength of this relationship is larger between poor-harsh and poor-temperate nations than between rich-harsh and rich-temperate nations. We find similar cross-level cross-national differences for the link between perceived ease of use (PEOU) and PU but not for the link between PEOU and BI. Implications for research and practice are discussed

Understanding Cross National Difference in Knowledge Seeking Behavioral Model: A Survival Perspective

Electronic Knowledge Repository (EKR) is one of the most commonly deployed knowledge management technologies, yet its success is hindered by employees’ underutilization and further complicated when implemented in the multinational context. To address these challenges, we propose a research model by conceptualizing employees’ knowledge seeking via EKR as a survival-centric behavior, identifying the technology acceptance model as the individual-level explanation for EKR use, and drawing on the thermal demands-resources theory for explaining cross national behavioral differences. Using hierarchical linear modeling, we tested the model with data from 1352 randomly sampled knowledge workers across 30 nations. The results reveal interesting cross national behavioral patterns. Specifically, thermal climates and national wealth at the macro-level interactively moderate individual-level relationships between perceived ease of use and perceived usefulness and between perceived usefulness and behavioral intention