arXiv:2607. 20705v1 Announce Type: cross Abstract: Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that converge slowly.
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arXiv:2607. 05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures.
arXiv:2607. 05955v1 Announce Type: cross Abstract: Interactive 3D segmentation aims to extract object masks in point clouds with minimal user clicks.
arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.
arXiv:2607. 08201v1 Announce Type: cross Abstract: Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity.
arXiv:2509. 23876v3 Announce Type: replace-cross Abstract: Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies between patches across timesteps introduced by progressive resolution scaling.
arXiv:2509. 10334v2 Announce Type: replace-cross Abstract: Vision Transformers (ViTs) have recently achieved strong results in semantic segmentation, yet their deployment on resource-constrained devices remains limited due to their high memory footprint and computational cost.