Signals 4 · free daily AI digest

Circumventing Magnitude Collapse via Modular Two-Stage Open-Set Recognition

Paper recorded by Signals 4 on 2026-11-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.CV · 计算机视觉 · first seen 2026-11-03

Abstract

Open-set recognition (OSR) requires neural networks to classify known classes while rejecting unknown samples, which is critical for real-world deployment. So far, OSR research studied and developed representation learning and postprocessing methods independently and their interactions remain unexplored. In this paper, we reconsider OSR as a modular two-stage pipeline and present the first systema

Read on arXiv →

#29 most recent of 710 cs.CV papers we have recorded · ↑ newer: Evaluating Depth-Augmented Visual Features for Cross-Dataset Lunar Tra · ↓ older: VidDub: Replacement-Aware Distillation for Two-Step Video Dubbing
Cite this page: Circumventing Magnitude Collapse via Modular Two-Stage Open-Set Recognition: the #29 most recent of 710 cs.CV papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/circumventing-magnitude-collapse-via-modular-two-stage-open-set-recognition.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions