ReCCur: A Recursive Corner-Case Curation Framework for Robust Vision-Language Understanding in Open and Edge Scenarios

• URL: http://arxiv.org/abs/2601.03011v1
• Date: Tue, 06 Jan 2026 13:36:43 GMT
• Title: ReCCur: A Recursive Corner-Case Curation Framework for Robust Vision-Language Understanding in Open and Edge Scenarios
• Authors: Yihan Wei, Shenghai Yuan, Tianchen Deng, Boyang Lou, Enwen Hu,
• Abstract summary: We present ReCCur, a framework that converts noisy web imagery into auditable fine-grained labels.<n>On realistic corner-case scenarios, ReCCur runs on consumer-grade GPUs, steadily improves purity and separability.
• Score: 14.85600144047706
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Corner cases are rare or extreme scenarios that drive real-world failures, but they are difficult to curate at scale: web data are noisy, labels are brittle, and edge deployments preclude large retraining. We present ReCCur (Recursive Corner-Case Curation), a low-compute framework that converts noisy web imagery into auditable fine-grained labels via a multi-agent recursive pipeline. First, large-scale data acquisition and filtering expands a domain vocabulary with a vision-language model (VLM), crawls the web, and enforces tri-modal (image, description, keyword) consistency with light human spot checks to yield refined candidates. Next, mixture-of-experts knowledge distillation uses complementary encoders (e.g., CLIP, DINOv2, BEiT) for kNN voting with dual-confidence activation and uncertainty sampling, converging to a high-precision set. Finally, region-evidence VLM adversarial labeling pairs a proposer (multi-granularity regions and semantic cues) with a validator (global and local chained consistency) to produce explainable labels and close the loop. On realistic corner-case scenarios (e.g., flooded-car inspection), ReCCur runs on consumer-grade GPUs, steadily improves purity and separability, and requires minimal human supervision, providing a practical substrate for downstream training and evaluation under resource constraints. Code and dataset will be released.

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