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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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樓主: centipede
51#
發(fā)表于 2025-3-30 10:40:50 | 只看該作者
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發(fā)表于 2025-3-30 14:11:38 | 只看該作者
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發(fā)表于 2025-3-30 20:12:29 | 只看該作者
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發(fā)表于 2025-3-30 23:53:43 | 只看該作者
https://doi.org/10.1007/978-3-319-47334-5esses. In the early route, intermediate outputs are consolidated via an anti-redundancy operation, enhancing their compatibility for subsequent interactions; thereby in the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead and regulate these fai
55#
發(fā)表于 2025-3-31 04:07:00 | 只看該作者
Tu?ba Ko?ak,Aytu? Altunda?,Thomas Hummele adaptation to fail in Mono 3Det. To handle this problem, we propose a novel .cular .est-.ime .daptation (.) method, based on two new strategies. 1) Reliability-driven adaptation: we empirically find that . and the optimization of high-score objects can .. Thus, we devise a self-adaptive strategy t
56#
發(fā)表于 2025-3-31 06:35:17 | 只看該作者
57#
發(fā)表于 2025-3-31 10:55:29 | 只看該作者
58#
發(fā)表于 2025-3-31 14:05:22 | 只看該作者
Serge Yan Landau,Giovanni Molleenabling the unified color NeRF reconstruction. Besides the view-independent color correction module for external differences, we predict a view-dependent function to minimize the color residual (including, .., specular and shading) to eliminate the impact of inherent attributes. We further describe
59#
發(fā)表于 2025-3-31 17:30:26 | 只看該作者
Zoochory: The Dispersal Of Plants By Animals support multi-task training. Tested across ten diverse 3D-VL datasets, . demonstrates impressive performance on these tasks, setting new records on most benchmarks. Particularly, . improves the state-of-the-art on ScanNet200 by 4.9% (AP25), ScanRefer by 5.4% (acc@0.5), Multi3DRefer by 11.7% (F1@0.5
60#
發(fā)表于 2025-3-31 23:22:05 | 只看該作者
Zoochory: The Dispersal Of Plants By Animalsing a minimal number of models to draw a more optimized-averaged model. We demonstrate the efficacy of Model Stock with fine-tuned models based upon pre-trained CLIP architectures, achieving remarkable performance on both ID and OOD tasks on the standard benchmarks, all while barely bringing extra c
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