The paper introduces Shortcut-Resistant CAM Distillation (SRCD), a plug‑and‑play framework that mitigates shortcut learning in long‑tailed recognition tasks. SRCD transfers object‑focused explanations from head classes to tail classes by aggregating Class Activation Maps (CAMs) into a shortcut‑resistant teacher and distilling it to tail‑class CAMs. The authors provide a theoretical analysis of shortcut reliance in CAM space and demonstrate consistent improvements over strong baselines on long‑tailed benchmarks.
By Wenhai Wan, Teng Zhang, Shao-Yuan Li, Xinrui Wang, Qiang-Sheng Hua, Songcan Chen
arXiv:2609.37331v1 Announce Type: new
Abstract: Balancing performance trade-offs on long-tailed data distributions remains a long-standing challenge in visual recognition. Existing methods mainly imp...
By Shenghan Chen, Yiming Liu, Zhipeng Deng, Haolin Wang, Jiale Zhou, Zhijian Wu, Xiankai Lu, Yafei Ou, Yefeng Zheng
Balancing performance trade-offs on long-tailed data distributions remains a long-standing challenge in visual recognition. Existing methods mainly improve tail classes through re-balancing, represent...
arXiv:2610.07903v1 Announce Type: new
Abstract: Adapting vision-language models to downstream tasks has achieved remarkable success by leveraging pseudo-labels generated from unlabeled data. Existing...
By Keliang Chen, Yaxin Hou, Hui Liu, Yuheng Jia
arXiv:2608.30699v1 Announce Type: cross
Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
By Yue Cheng, Jiajun Zhang, Xiaohui Gao, Weiwei Xing, Zhanxing Zhu
arXiv:2610.02117v1 Announce Type: cross
Abstract: On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a froze...
By Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome, Spyros Gidaris
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
arXiv:2608.28216v1 Announce Type: new
Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is ab...
By Kishor Datta Gupta, Ahmed Rafi Hasan, Md. Mahfuzur Rahman, Md. Sadman Haque, Mohd Ariful Haque
Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background correlations, resulting in predictions driven by co...
arXiv:2608.29996v1 Announce Type: cross
Abstract: Large Vision-Language Models (LVLMs) achieve strong performance across many multimodal tasks; however, they often exploit spurious object-background...
By Aditi Sarker, Nazreen Shah, Rafi Ibn Sultan, Rhongho Jang, Dongxiao Zhu, Prashant Khanduri
Falcon Perception-HD applies reinforcement learning (GRPO) to autoregressive perception models, aligning them directly with precision and recall metrics rather than relying on maximum‑likelihood fine‑tuning. The RL framework introduces reward design for set‑structured outputs and multi‑head sampling control, enabling state‑of‑the‑art performance in very dense scenes (up to 500 objects) and eliminating common issues such as mask repetitions, NMS, and coordinate deduplication. Hybrid self‑annotation pipelines tailored for difficult referring expressions and dense scenes further boost RL training, with improvements observed across all difficulty levels on PBench and SACO‑Gold, and the model preserves object existence knowledge without negative samples.
arXiv:2610.09502v1 Announce Type: new
Abstract: Open-vocabulary detection (OVD) recognizes categories unseen during training through textual category queries, yet achieving strong generalization with...
By Yupeng Zhang, Ziyi Zhao, Juntao Cheng, Sheng Wang, Ningnan Guo, Ruize Han, Liang Wan