arXiv:2607. 14509v1 Announce Type: cross Abstract: This paper describes DS@GT ARC's third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict every species present in high-resolution (~3000 x 3000 pixel) plot photographs while training only on single-label images of individual plants.
By Alper Erten, Murilo Gustineli, Adrian Cheung
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:2605.17630v3 Announce Type: replace
Abstract: Frozen segmentation foundation models often fail when the target class appears in a form that is weakly represented during pretraining. To address...
By Abderrahmene Boudiaf, Irfan Hussain, Sajid Javed
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
The paper introduces RSMEB, a unified benchmark for remote‑sensing multimodal retrieval that evaluates both cross‑modal and interleaved retrieval across 21 tasks under a single ranking protocol. It also presents VLM2GeoVec, an instruction‑conditioned single‑encoder model that embeds image, text, bounding‑box, and geo‑coordinate tokens into one sequence and achieves state‑of‑the‑art performance on region‑caption, referring‑expression, and semantic geo‑aware retrieval while remaining competitive on conventional tasks. The authors provide code, checkpoints, and data on GitHub to facilitate reproducibility.
By Emanuel S\'anchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan, Yonghao Xu, Michael Felsberg
arXiv:2609.05762v1 Announce Type: cross
Abstract: UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such...
By Tarek Rahman, Nazim-E-Alam, Md Kishor Morol, Jannatun Noor
arXiv:2602.19736v3 Announce Type: replace
Abstract: Diffusion models now give the best perceptual quality in super-resolution (SR), but their architecture and training confine them to small fixed cro...
By Shoukun Sun, Zhe Wang, Xiang Que, Jiyin Zhang, Xiaogang Ma
The paper addresses lifelong aerial autonomy by treating visual place recognition (VPR) as a mission‑based domain‑incremental learning problem. It introduces a heterogeneous memory framework that first trains on a static satellite exemplar memory and then uses a bounded replay buffer to retain selected airborne observations across missions. The proposed DBS‑Hybrid replay strategy, which blends prototype‑based diversity trimming with representative‑first feature‑space coverage, outperforms baseline methods in accuracy, generalization, and knowledge retention across multiple UAV missions.
By Xingyu Shao, Zhiqiang Yan, Liangzheng Sun, Mengfan He, Chao Chen, Jinhui Zhang, Chunyu Li, Ziyang Meng
arXiv:2608.21761v1 Announce Type: new
Abstract: Large collections of street-view imagery provide rich visual information about urban environments, but extracting fine-grained geographic information f...
By Changyu Lee, Yeonsoo Park, Abdullah Alfarrarjeh, Seon Ho Kim
The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.
By Bingyu Li, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
arXiv:2604. 27128v2 Announce Type: replace-cross Abstract: Foundation-model pipelines for individual-level livestock monitoring -- combining open-vocabulary detection, promptable video segmentation, and self-supervised visual embeddings -- have raised the accuracy ceiling of precision livestock farming (PLF), but their GPU memory budgets exceed the envelope of commodity edge accelerators.
By Haiyu Yang, Miel Hostens