Titlebook: Industrial Craft in Australia; Oral Histories of Cr Jesse Adams Stein Book 2021 The Editor(s) (if applicable) and The Author(s), under excl

弯腰

manifestation

Jesse Adams Steinexample the Social Force Model (SFM). This class of approaches describes the movements and local interactions among individuals in crowds by means of repulsive and attractive forces. Despite their promising performance, recent socio-psychology studies have shown that current SFM-based methods may no

入伍仪式

Jesse Adams Steinrnels on learned representations is limited. In this work, we explore and employ the relationship between shape of kernels which define receptive fields (RFs) in CNNs for learning of feature representations and image classification. For this purpose, we present a feature visualization method for vis

不幸的人

Jesse Adams Steinf models perform differently in terms of effectiveness and robustness under different challenging situations. This work exploits the advantages of both models. A subspace model, from a global perspective, is learned from previously obtained targets via rank-minimization to address the tracking, and

躲债

Jesse Adams Steinobustness, cutting edge supervised learning approaches rely on large training datasets of face images captured in the wild. While impressive tracking quality has been demonstrated for faces that are largely visible, any occlusion due to hair, accessories, or hand-to-face gestures would result in sig

GOUGE

Jesse Adams Stein representation. The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data. The factorization is provi

opprobrious

Jesse Adams Steinations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling

出价

derstanding the movements of objects as a result of applying external forces to them. For a given force vector applied to a specific location in an image, our goal is to predict long-term sequential movements caused by that force. Doing so entails reasoning about scene geometry, objects, their attri

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必死