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Diversity Without Labels: Task-Agnostic Coreset Selection Across Perception and Action

Paper recorded by Signals 4 on 2026-10-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-16 on arXiv · recorded by Signals 4 on 2026-10-19

Category: cs.CV · 计算机视觉 · first seen 2026-10-19

Abstract

While data collection for model training is relatively easy, curating high-quality datasets from large collections is inherently challenging. Coreset estimation methods address this problem by identifying the best data subsets to train models on. However, existing approaches often require proxy model training, extensive annotations, or task-specific tailoring. Unsupervised alternatives typically e

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#246 most recent of 756 cs.CV papers we have recorded · ↑ newer: A Language-Informed Loss for Visual Generation · ↓ older: Stereo LWIR-RGB Automatic Labeling for Low-Light Camera-Based VRU Dete
Cite this page: Diversity Without Labels: Task-Agnostic Coreset Selection Across Perception and Action: the #246 most recent of 756 cs.CV papers we have recorded (as of 2026-10-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/diversity-without-labels-task-agnostic-coreset-selection-across-perception-and-a.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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