Panther
发表于 2025-3-23 13:12:26
FlowCon: Out-of-Distribution Detection Using Flow-Based Contrastive Learning,methods modify softmax scores fine-tuned on outlier data or leverage intermediate feature layers to identify distinctive patterns between In-Distribution (ID) and OOD samples. Other methods focus on employing diverse OOD samples to learn discrepancies between ID and OOD. These techniques, however, a
灵敏
发表于 2025-3-23 17:23:35
,LEIA: Latent View-Invariant Embeddings for Implicit 3D Articulation,ending NeRFs to model dynamic objects or object articulations remains a challenging problem. Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories,
粗糙滥制
发表于 2025-3-23 18:34:22
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蘑菇
发表于 2025-3-23 22:52:16
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佛刊
发表于 2025-3-24 02:22:38
,CityGaussian: Real-Time High-Quality Large-Scale Scene Rendering with Gaussians,r, effectively training large-scale 3DGS and rendering it in real-time across various scales remains challenging. This paper introduces CityGaussian (CityGS), which employs a novel divide-and-conquer training approach and Level-of-Detail (LoD) strategy for efficient large-scale 3DGS training and ren
BAIT
发表于 2025-3-24 08:26:35
,Bayesian Evidential Deep Learning for Online Action Detection,l for real-world applications. In this paper, we introduce Bayesian Evidential Deep Learning (BEDL), an efficient and generalizable framework for online action detection and uncertainty quantification. Specifically, we combine Bayesian neural networks and evidential deep learning by a teacher-studen
OTHER
发表于 2025-3-24 14:24:56
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悠然
发表于 2025-3-24 14:58:55
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曲解
发表于 2025-3-24 19:42:19
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Legend
发表于 2025-3-25 02:47:52
0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. The papers deal with topics such as computer vision; machine learning; deep neural netwo