Hard Norm Alignment loss (HNA): apply the HNA loss (Eq. HMDB, which demonstrates the effectiveness of our model. First, we drop feature from a specific spatial group. Method UCF HMDB STDN-T -1 59.2 STDN-T -2 58.1 STDN-T -3 59.4 STDN-T -4 58.9 Full STDN 60.2 Second, we drop feature from a space scale. In our main manuscript, we conduct all experiments based on ResNet-50.

However, existing video classification models rely on the i.i.d.

To this end, we propose SpatialPIN, a framework that utilizes progressive prompting and interactions between VLMs and 2D/3D foundation models as "free lunch" to enhance spatial reasoning capabilities

Since the inception of our planet, the meteorological environment, as reflected through spatio-temporal data, has always been a fundamental factor influencing human life, socio-economic progress, and ecological conservation.

The validation of global climate models is crucial to ensure the accuracy and efficacy of model output. We introduce the spherical convolutional Wasserstein distance to more comprehensively measure differences between climate models and reanalysis data.

This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space.

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