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

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MELD

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

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Tuğba Koçak,Aytuğ Altundağ,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

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FICE

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

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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

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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