PredErase is a training‑free method that removes objects and their photometric effects by guiding a frozen Fill model with predictive latent cues. It expands the user‑provided mask to a contact‑band region, uses I‑JEPA to generate a context‑conditioned target for the hole, and aligns Fill’s completions with this target while keeping surrounding pixels fixed. On benchmarks such as RemovalBench, RORD‑Val, and DEFACTO‑Val, PredErase improves the native FLUX.2 backbone for instance‑only masks, though supervised removers still outperform it on full‑image metrics.
By Waikit Xiu, Qiang Lu, Junbiao Chen, Xiying Li
The paper introduces SphereTrust, a method that uses frozen self‑supervised hyperspherical features to evaluate and rank candidate masks produced by foundation segmenters like SAM. By measuring angular contrast, foreground coverage, and image‑frame contact, SphereTrust can select high‑quality masks in 0.55 s per image and outperforms existing baselines on multiple segmentation tasks. The selected masks are then used as priors to train student models, improving performance on several benchmark datasets.
By Xinge Guo, Fengyang Xiao, Dingming Zhang, Yuhan Chen, Rihan Zhang, Xingjian Li, Tianyang Wang, Chunming He, Sina Farsiu
arXiv:2609.07915v1 Announce Type: cross
Abstract: Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or fie...
By Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Jia Chen, Evangelos E. Papalexakis
The paper presents a method for distilling a large 300‑million‑parameter geospatial foundation model (Prithvi‑EO‑2.0) into a compact 0.7‑million‑parameter EfficientViT‑B0 student for flood segmentation. By using the teacher to supervise additional unlabeled Sentinel‑2 imagery, the student’s training set expands without new manual labels, achieving competitive performance on Sen1Floods11 and STURM‑Flood while remaining smaller and faster. After quantization, the student runs as a 1.5‑MB INT8 TensorRT engine on a Jetson Xavier NX, processing 512×512 images in 5.57 ms with ~14 MB of memory.
By Fabian Schmalstieg, Karsten Mueller, Wojciech Samek
arXiv:2606. 15427v1 Announce Type: cross Abstract: Spaceborne inspection systems often deploy perception models prior to launch, after which updating model weights or expanding fixed label sets becomes operationally impractical.
By Nicholas A. Welsh, Lennon J. Shikhman, Monty Nehru Attazs, Seemanthini K. Putane, Van Minh Nguyen, Ryan T. White
Groundbench is a new benchmark that evaluates vision‑language models on multi‑resolution polygon grounding, using the same 1,500 image‑expression‑referent triples but targeting exact‑N polygons with five different vertex budgets. It audits both filled‑region intersection‑over‑union (IoU) and legal‑polygon completion, revealing that the best models achieve 88.2 box IoU and 97.1 accuracy at IoU ≥ 0.5, while direct polygon predictions lag at 57.7 and 69.2. The study shows performance is non‑monotonic across budgets, collapses at the densest budget due to legality failures, and highlights that false spatial cues hurt more than false colour cues, underscoring an operational geometry gap beyond latent boundary perception.
By Zhonghan Bian, Zhenran Wang, Jinsong Li, Zhangyang Qi
arXiv:2607. 03869v1 Announce Type: cross Abstract: Referring remote sensing image segmentation isolates the object named by a natural-language expression in an aerial image.
By Yuhang Jiang, Guohui Deng, Miaozhong Xu, Chao Ruan, Jinling Zhao, Linsheng Huang
arXiv:2606. 28410v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-world understanding.
By Shanwen Wang, Xin Sun, Sirui Wang, Xiao Xiang Zhu
arXiv:2608. 09101v1 Announce Type: cross Abstract: Semantic segmentation models are trained and evaluated against human-drawn masks, yet remote-sensing annotations are often coarse, incomplete, or misaligned; high overlap scores may then reflect agreement with imperfect labels rather than faithfulness to the image, creating an evaluation paradox.
By Shuaishuai Cao, Shuwei Peng, Meng Tang, Min Huang, Youjin Wang, Jie Chen, Jing Ouyang, Zhiwei Zhai
arXiv:2606.18623v2 Announce Type: replace
Abstract: Gaussian segmentation is usually posed as transferring object knowledge from 2D foundation models into a 3D representation. This leaves a fundament...
By Mohamed Rayan Barhdadi, Hasan Yazar, Erchin Serpedin, Mehmet Tuncel, Hasan Kurban
arXiv:2605.19410v2 Announce Type: replace
Abstract: Segmentation has become easy when the concept is known, requiring retrieval of a learned visual grounding from text. It remains hard for open ad-ho...
By Zilin Wang, Stella X. Yu
XClipGS introduces an exact half‑space clipping method for Gaussian‑splatting of medical volumes, enabling precise rendering of clipped anatomy without learned clipping parameters. The technique factorizes the ray integral into a 2D footprint and a conditional Gaussian CDF, remaining differentiable and efficient. Evaluated on eight CT and MRI datasets, XClipGS outperforms prior methods in PSNR, SSIM, and leakage while achieving over 650 FPS.
By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Chaoyi Zhou, Terrence Chen, Ziyan Wu