REP
发表于 2025-3-27 00:23:57
Eckhard Gruberisting UDA methods fail to perform well due to the lack of instructions, leading their models to overlook discrepancies within all adverse scenes. To tackle this, we propose CoDA which instructs models to distinguish, focus, and learn from these discrepancies at scene and image levels. Specifically,
Circumscribe
发表于 2025-3-27 03:44:19
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jaundiced
发表于 2025-3-27 08:21:44
Ulrike Baureithelminate redundant data for faster processing without compromising accuracy. Previous methods are often architecture-specific or necessitate re-training, restricting their applicability with frequent model updates. To solve this, we first introduce a novel property of lightweight ConvNets: their abili
Aura231
发表于 2025-3-27 13:29:38
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Aggregate
发表于 2025-3-27 16:44:40
Dirk Hallenberger numerous 2D anomaly detection methods have been proposed and have achieved promising results, however, using only the 2D RGB data as input is not sufficient to identify imperceptible geometric surface anomalies. Hence, in this work, we focus on multi-modal anomaly detection. Specifically, we invest
chandel
发表于 2025-3-27 20:29:03
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开始发作
发表于 2025-3-27 22:31:40
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Statins
发表于 2025-3-28 05:45:51
Hugh Ridleypproaches have made great progress, but are typically hindered by the need for large datasets of either pose-labelled real images or carefully tuned photorealistic simulators. This can be avoided by using only geometry inputs such as depth images to reduce the domain-gap but these approaches suffer
plasma-cells
发表于 2025-3-28 06:21:36
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lambaste
发表于 2025-3-28 12:28:14
Almut Todorowimage and text. However, video making it challenging for LVLMs to perform adequately due to the complexity of the relationship between language and spatial-temporal data structure. Recent Large Video-Language Models (LVidLMs) align feature of static visual data like image into latent space of langua