arXiv:2609.24276v1 Announce Type: new
Abstract: Hyperbolic vision-language models (VLMs) represent image and text features in a geometry naturally suited to hierarchy, but their adaptation to downstr...
By Andro Erdelez, Pascal Mettes, Behzad Bozorgtabar
Frozen visual foundation models provide transferable features for visual place recognition, but fixed aggregation can suppress useful distinctions in new environments. We introduce TFA, a reliability-...
The paper introduces TFA, a training‑free aggregation technique that calibrates frozen visual foundation models for visual place recognition. TFA uses cross‑codebook agreement, retrieval coverage, and spectral statistics to adjust residual assignment, spectral shaping, and global‑feature fusion without requiring place labels or task‑specific weights. Experiments with a DINOv2‑B backbone show significant Recall@1 gains over existing training‑free methods across multiple benchmarks, demonstrating that reliability‑guided aggregation can unlock additional retrieval performance from frozen representations.
By Xin Li, Zhimin Mao, Shang Wang, Siyuan Duan, Geng Zhang
arXiv:2607. 05268v1 Announce Type: cross Abstract: Whether a hyperbolic representation model uses its geometry cannot be read off its curvature parameter: what matters is the dimensionless operating point $\sqrt{c}\rho$ and whether the radial and cone machinery is active there.
By Jaeyoung Kim, Eunseok Kim, Dongsuk Jang
arXiv:2609. 11916v1 Announce Type: new Abstract: Camera traps often run in the field on edge hardware with limited or no connectivity, making small, locally-deployable vision-language models (VLMs) -- not frontier-scale ones -- the practically relevant class to evaluate for species identification.
By William Zhou, Mayukha Siripuram, Xiao Yan, Ziqi Liu, Yi Ding
The paper introduces CERES, a closed‑loop multimodal indexing framework that addresses semantic collapse in multimodal generation by building a three‑level semantic pyramid and using scale‑routed cross‑attention to generate images that remain retrievable by their original queries. CERES employs a co‑occurrence‑aware router, a lightweight U‑Net generator, and a soft‑Jaccard coverage objective to ensure generated images cover the intended concepts, verified by re‑indexing with a frozen vision‑language model and an external DINOv2 probe. Experiments on four pansharpening benchmarks show state‑of‑the‑art performance, especially under extreme scale variation, and significant improvements in concept‑query retrieval and image‑text ranking metrics.
By Guangyuan Dong, Chuang Liu, Haoyu Wang, Yangchen Zeng, Jiaqi Zhang, Li Jiuxing, Xiaoyang Yu, Pinlong Zhao, Yuchao Hou, Ziwei Li, Zheng Lin, Alexander Lim Han Yang, Yusen Wu