Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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obstinate

,LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds,r, arguably due to the higher-dimensional nature of the data (as compared to images), existing neural architectures exhibit a large variety in their designs, including but not limited to the views considered, the format of the neural features, and the neural operations used. Lack of a unified framew

Tincture

,Uncertainty-DTW for Time Series and Sequences,ustering time series or even matching sequence pairs in few-shot action recognition. The transportation plan of DTW contains a set of paths; each path matches frames between two sequences under a varying degree of time warping, to account for varying temporal intra-class dynamics of actions. However

cogitate

Black-Box Few-Shot Knowledge Distillation,nal KD methods require lots of . training samples and a . teacher (parameters are accessible) to train a good student. However, these resources are not always available in real-world applications. The distillation process often happens at an external party side where we do not have access to much da

senile-dementia

Revisiting Batch Norm Initialization,ral networks. Standard initialization of each BN in a network sets the affine transformation scale and shift to 1 and 0, respectively. However, after training we have observed that these parameters do not alter much from their initialization. Furthermore, we have noticed that the normalization proce

Kaleidoscope

,SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling,ng layers are not learned, and thus cannot preserve important information. As another dimension reduction method, adaptive sampling weights and processes regions that are relevant to the task, and is thus able to better preserve useful information. However, the use of adaptive sampling has been limi

偶然

,Filter Pruning via Feature Discrimination in Deep Neural Networks,g, We first propose a feature discrimination based filter importance criterion, namely Receptive Field Criterion (RFC). It turns the maximum activation responses that characterize the receptive field into probabilities, then measure the filter importance by the distribution of these probabilities fr

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Notorious

,BA-Net: Bridge Attention for Deep Convolutional Neural Networks,ue to heavy feature compression in the attention layer. This paper proposes a simple and general approach named Bridge Attention to address this issue. As a new idea, BA-Net straightforwardly integrates features from previous layers and effectively promotes information interchange. Only simple strat