arXiv:2607. 23981v1 Announce Type: cross Abstract: Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes.
By Weijun Tian, Rui Liu
Vague2Detect is a hybrid pipeline that improves object detection for ambiguous prompts by combining a fine‑tuned Sentence‑BERT to retrieve candidates from a structured household knowledge base, YOLO‑World to verify their presence in images, and a GPT‑3.5‑turbo fallback to generate new candidate descriptions when prompts fall outside the knowledge base. On a benchmark of household scenes, Vague2Detect raises the vague prompt success rate from 32% (YOLO‑World alone) to 61% with high precision, and up to 85% when the GPT fallback is used.
By Ibrohimjon Muminov (Dongguk University, Seoul, South Korea), Jihie Kim (Dongguk University, Seoul, South Korea)
The paper demonstrates that object detection benchmarks suffer from incomplete annotations, with re-annotation of COCO, Pascal VOC, Cityscapes, and KITTI revealing up to a 60% increase in detected objects, especially small, occluded, or densely packed instances. The authors propose a scalable annotation pipeline that uses multiple annotators per object to capture uncertainty and improve recall, and they introduce two new large-scale benchmarks: an uncertainty-aware detection benchmark and a label error detection benchmark based on real errors. Their findings show that benchmark performance is highly sensitive to annotation quality, yet model rankings remain largely unchanged, highlighting the need for uncertainty-aware evaluation to better reflect real-world ambiguity.
By Sarina Penquitt, Jonathan Klees, Antonia van Betteray, Parssa Jashnieh, Peter Stehr, Matthias Rottmann, Lars Schmarje
The paper introduces Structured Prior Knowledge (SPK), a framework that extracts and organizes latent priors from pretrained object detectors to improve out-of-distribution (OoD) detection. SPK uses in-distribution data and hallucination-inducing samples to elicit part-level semantic concepts, then combines these with geometric and contextual priors into a compact five-dimensional representation. Experiments across various detector architectures and OoD benchmarks show that SPK achieves state-of-the-art performance, demonstrating that pretrained detectors encode richer latent knowledge than previously exploited.
By Changshun Wu, Weicheng He, Xiaowei Huang, Saddek Bensalem
Real-world detectors must often interpret functional or ambiguous prompts, yet conventional models such as YOLO remain restricted to fixed class lists. Even open-vocabulary models like YOLO-World freq...
arXiv:2609.23431v1 Announce Type: new
Abstract: Human-object interaction (HOI) detection requires grounding an interacting human-object pair and recognizing the verb that links them, often under seve...
By Junwen Chen, Keiji Yanai