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

寬敞

Ambulanzmanual P?diatrie von A-Znd navigation within an environment. While modern AVE methods have demonstrated impressive performance, they are constrained to fixed-scale glimpses from rigid grids. In contrast, existing mobile platforms equipped with optical zoom capabilities can capture glimpses of arbitrary positions and scales

BOOST

crumble

https://doi.org/10.1007/978-3-642-24683-8y, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the

BRIDE

Ambulanzmanual P?diatrie von A-Zusion model that dynamically adapts to scene graphs. Existing methods struggle to handle scene graphs due to varying numbers of nodes, multiple edge combinations, and manipulator-induced node-edge operations. EchoScene overcomes this by associating each node with a denoising process and enables coll

胎兒

緊張過(guò)度

Ambulanzmanual P?diatrie von A-Zn Localization (OnTAL), extend this approach to instance-level predictions. However, existing methods mainly focus on short-term context, neglecting historical information. To address this, we introduce the History-Augmented Anchor Transformer (HAT) Framework for OnTAL. By integrating historical con

fiscal

https://doi.org/10.1007/978-3-642-24683-8 deep net to learn higher-level representations. Contrary to this motivation, we hypothesize that the discarded activations are useful and can be incorporated on the fly to improve models’ prediction. To validate our hypothesis, we propose a search and aggregate method to find useful activation maps

foliage

改變

Ambulanzmanual P?diatrie von A-Z Recent efforts tackle this challenge by adopting an analysis-by-synthesis paradigm to learn 3D reconstruction with only 2D annotations. However, existing methods face limitations in both shape reconstruction and texture generation. This paper introduces an innovative Analysis-by-Synthesis Transform

相一致

Ambulanzmanual P?diatrie von A-Zs after training. This leads to the problem of . (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model’s utility post-unlearning. Despite various MU methods for data influence erasure, evaluations have largely focused on . data forgetti