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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,Static and?Dynamic Concepts for?Self-supervised Video Representation Learning,pose to first learn general visual concepts then attend to discriminative local areas for video understanding. Specifically, we utilize static frame and frame difference to help decouple static and dynamic concepts, and respectively align the concept distributions in latent space. We add diversity a

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SphereFed: Hyperspherical Federated Learning,challenge is the handling of non-. (independent identically distributed) data across multiple clients that may induce disparities of their local features. We introduce the Hyperspherical Federated Learning (SphereFed) framework to address the non-. issue by constraining learned representations of da

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,Posterior Refinement on?Metric Matrix Improves Generalization Bound in?Metric Learning,trained on finite known data can achieve similitude performance on infinite unseen data. While considerable efforts have been made to bound the generalization gap by enhancing the model architecture and training protocol a priori in the training phase, none of them notice that a lightweight posterio

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,CoSCL: Cooperation of?Small Continual Learners is Stronger Than a?Big One, general, learning all tasks with a shared set of parameters suffers from severe interference between tasks; while learning each task with a dedicated parameter subspace is limited by scalability. In this work, we theoretically analyze the generalization errors for learning plasticity and memory sta

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,Manifold Adversarial Learning for?Cross-domain 3D Shape Representation,generalization to out-of-distribution 3D point clouds remains challenging for DNNs. As annotating large-scale point clouds is prohibitively expensive or even impossible, strategies for generalizing DNN models to unseen domains of point clouds without access to those domains during training are urgen

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,LoRD: Local 4D Implicit Representation for?High-Fidelity Dynamic Human Modeling, to missing surface details and accumulating tracking error. While many deep local representations have shown promising results for 3D shape modeling, their 4D counterpart does not exist yet. In this paper, we fill this blank by proposing a novel .cal 4D implicit .epresentation for .ynamic clothed h