Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.
By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella
Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes. PROB improves unknown discovery by modeling class-agnostic probabilistic objectness in the decoder-query space.
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
arXiv:2607. 05978v1 Announce Type: cross Abstract: Multimodal large language models can emit localized predictions, bounding boxes for objects and temporal windows for video and audio events, but they hallucinate these regions prolifically.
By Daniel Shalam, Emanuel Ben Baruch, Avi Ben Cohen, Tal Remez
arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.
By Seung Il Lee, Qinqian Lei, Daguang Xu, Dong Yang, Robby T. Tan, Yixin Chen, Bo Wang
arXiv:2501.12632v3 Announce Type: replace-cross
Abstract: Weakly supervised object localization (WSOL) models can predict both the object class and the spatial regions corresponding to the object, wi...
By Shakeeb Murtaza, Soufiane Belharbi, Alexis Guichemerre, Marco Pedersoli, Eric Granger
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
arXiv:2607.09086v2 Announce Type: replace
Abstract: We present Subtoken Vision Transformer (SubViT), a selective image tokenization method for fine-grained visual recognition. Standard Vision Transfo...
By Jie Zhu, Ivy Zhang, Minchul Kim, Xiaoming Liu
CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection introduces a unified inference-time framework that addresses semantic ambiguity and over-suppression in multimodal OWOD systems. It comprises Cross-Modal Joint Confidence Calibration, Uncertainty-Guided Universal Objectness Enhancement, and Dynamic Outlier Suppression via Confidence Margin. Experiments on the Real-World Detection benchmark with the OWL‑ViT L/14 backbone show CODE achieving 21.7 U‑mAP and 40.8 K‑mAP, surpassing prior state‑of‑the‑art results by 2.6 and 2.3 points respectively.
By Hao Xu, Zhaoning Shi, Hehe Jin, Bo Ma
arXiv:2606.16996v2 Announce Type: replace-cross
Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabu...
By Tran Dinh Tien, Zhiqiang Shen
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
The paper introduces Vision‑RL2, a region‑level reinforcement learning approach that optimizes a lightweight proposal network for fine‑grained multimodal large language model (MLLM) perception. By treating coherent image regions as actions and scoring them with a frozen MLLM reader, the method selectively focuses visual resolution on evidence, reducing token usage while improving accuracy across multiple benchmarks and backbones. The approach eliminates the need for region annotations, response sampling, or reasoning trajectories, and the refined proposals enable sparse encoding that magnifies relevant evidence.
By Yuheng Shi, Xiaohuan Pei, Minjing Dong, Chang Xu