Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks?

arXiv:2609.30080 · cs.CV · Submitted 2026-09-24 · Read on arXiv

cs.CV

Submitted: 2026-09-24

Updated: 2026-09-24

Comments: 29 pages, 11 figures, 19 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 31× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 16× in this paper
• Ablation — 8× in this paper · explained in 1 episode
• Core — 8× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• In International Conference on Learning Representations — 5× in this paper
• CVF International Conference on Computer Vision — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Clustering — 3× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 3× in this paper
• Segment Anything Model — 3× in this paper
• Distillation — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Semi-supervised Learning — 2× in this paper · explained in 4 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• International Joint Conference on Artificial Intelligence — 1× in this paper
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Mask Generation — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Qwen2.5-VL Technical Report
• SAM 3: Segment Anything with Concepts
• A Survey of Camouflaged Object Detection and Beyond
• Learning Accurate Segmentation Purely from Self-Supervision

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models