OV-DQUO is an open-vocabulary detection framework that learns from open-world unknown objects through wildcard matching and contrastive denoising training methods, mitigating performance degradation ...
Mechanism-level reproduction of Google's Nested Learning (HOPE) architecture (HOPE blocks, CMS, and Self‑Modifying TITANs), matching the quality bar set by lucidrains' TITAN reference while remaining ...
Abstract: Existing unsupervised salient object detection (USOD) methods usually rely on low-level saliency priors, such as center and background priors, to detect salient objects, resulting in ...
Abstract: Spatial computing could change our interaction with the physical world, yet development tools still largely apply to outdated methods, representing 3D objects with 2D text in code editors.
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