Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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Allgemeine Immunologie und Immunpathologieng additional generic features free of the source model’s CDS bias. This provides a “second-opinion” regarding which nearest neighbors are more suitable for adaptation. We evaluate our method?using various types of features, datasets and tasks, outperforming previous methods in SFDA under CDS. Our c

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https://doi.org/10.1007/978-3-642-75228-5dividual features and a try-on module refining the attire?and generating missing parts integrated with a mask-aware post-processing technique ensuring the integrity of the individual’s identity. It demonstrates impressive results, surpassing?the state-of-the-art in speed by nearly 20 times during in

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https://doi.org/10.1007/978-3-642-91030-2 fusion ability while still enjoying efficient inference latency and memory consumption during inference. Extensive experiments on the nuScenes benchmark demonstrate its effectiveness, achieving a new state-of-the-art performance of 57.4. mAP and 65.1. NDS on the test set. The real-time version (25.

CHIP

S?tze und Texte verstehen und produzierenis disentangled from the shape latent space. The proposed deformation representation, which works by predicting skinning weights and rigid transformations for multiple parts, also has advantages over the deformation modules of existing 4D representations in understanding the structure of shapes. In

Cantankerous

Volition und kognitive Kontrollerial inputs, significantly enhancing its overall robustness. By integrating these methodologies, DGAD significantly improves upon the accuracy of clean data and fortifies the model’s defenses against sophisticated adversarial threats. Our experimental validation on CIFAR10, CIFAR100, and Tiny ImageN

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Multisensorische Informationsverarbeitungrmation. The two volumes are finally fused to fully represent motion and appearance. This divide-and-conquer strategy leads to more robust tracking through occlusions and deformations and meanwhile obtains decomposed appearances. We conduct evaluations on the TAP-Vid benchmark. The results demonstra

PHON

Kategorisierung und Wissenserwerbdimensional space obtained through ch1tspsSNE, SLS facilitates informed pruning decisions. Our study reveals that layer-wise pruning, with a focus on storing pruning indices, addresses storage volume concerns. Notably, mainstream Layer-wise pruning methods may not be suitable for assessing layer imp