Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.
arXiv:2609.13318v1 Announce Type: cross Abstract: 3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tig...
arXiv:2609.14080v1 Announce Type: new Abstract: Promptable models such as microSAM segment electron microscopy (EM) images from point prompts, but automation requires generating prompts without user...
arXiv:2609.15579v1 Announce Type: new Abstract: An airborne sensor on a contested link cannot send video, so the appealing move is to send findings instead and report the ratio between the two. We ev...
arXiv:2609.13733v1 Announce Type: new Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate came...
MedVA is an end‑to‑end neuro‑symbolic agentic system designed to streamline medical volume visualization. It combines a neuro‑symbolic intent formulation agent that refines natural‑language requests with symbolic reasoning, a multi‑model ROI identification agent that uses pretrained medical segmentation models to locate specified regions, and an objective‑driven visualization optimization agent that evaluates ROI visibility using a volume‑based objective. Extensive evaluations and a formative user study demonstrate the system’s effectiveness and high usability across users with varying expertise.
arXiv:2609.13654v1 Announce Type: new Abstract: Pretrained foundation models (FMs) have achieved remarkable success in computer vision, yet their high fine-tuning cost limits practical deployment. Pa...
arXiv:2609.13815v1 Announce Type: new Abstract: Text-centric Visual Question Answering (VQA) requires reading and reasoning over text embedded in images, a task made substantially harder when images...
arXiv:2609.13823v1 Announce Type: new Abstract: Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on...
arXiv:2609.13706v1 Announce Type: new Abstract: Co-salient object detection (Co-SOD) requires a model to find foreground regions that are salient in individual images and supported by the image group...
arXiv:2609.13268v1 Announce Type: new Abstract: Annual reports, diligence packs, and infographic dashboards bury numbers in page images: axes, cell grids, and footnotes that OCR pipelines flatten and...
arXiv:2609.13617v1 Announce Type: new Abstract: We evaluate whether the improvements advertised for ChatGPT Images 2.5 translate into better performance on forgery tasks with predetermined answers. W...
The paper introduces Learning Neighbor Regions (LNR), a coarse‑to‑fine framework for robust multi‑model fitting in noisy environments. LNR’s coarse module uses a neural network to analyze geometric features of minimum sets, selecting a small number of promising hypotheses to improve efficiency. The fine module encodes neighbor region features for each hypothesis, allowing simultaneous refinement and scoring while avoiding differentiation of the sampling process and solvers. Experiments on four classic tasks show state‑of‑the‑art performance and suggest easy adaptation to other robust fitting problems.
arXiv:2609.13665v1 Announce Type: new Abstract: Accurate cell segmentation remains a major bottleneck in subcellular spatial transcriptomics (SST), in which morphological images and spatially resolve...
arXiv:2609.14466v1 Announce Type: new Abstract: Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the obse...
arXiv:2609.14010v1 Announce Type: new Abstract: We present CirrGuide, a deep cascaded framework for cirrhotic liver segmentation and severity classification. Cirrhosis causes progressive structural c...
arXiv:2609.13226v1 Announce Type: new Abstract: Machine learning for neglected tropical diseases is limited by data, not algorithms: public annotated image sets for leprosy (Hansen's disease) number...
Rapid advancements in vision-language models have propelled Referring Remote Sensing Image Segmentation (RRSIS) to the forefront of Earth observation. However, practical deployments suffer severe perf...
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...
The paper introduces CP‑BG‑Bench, a paired‑view evaluation framework for Cell Painting vision encoders that fixes a central cell across four matched views (raw crop, segmented, and density‑augmented variants). Using this framework on three datasets and three encoders, the authors show that standard single‑metric rankings (e.g., replicate mAP) vary systematically across protocols, revealing disagreements along axes of cell versus background, morphology versus context, and within‑study versus across‑batch performance. The study demonstrates that segmented views can outperform crops in certain tasks and that background‑driven gains are largely determined by experimental design rather than encoder choice.
The paper introduces SV-Cine, a cardiac MRI segmentation framework tailored for single ventricle physiology (SVP). It combines a generative data augmentation pipeline that creates synthetic 3D cardiac meshes and MRI, with a diagnosis-conditioned adaptation of the CineMA foundation model that uses patient-level diagnostic information to improve segmentation. Evaluations on an internal cohort show high Dice scores for left and right ventricles, outperforming nnU-Net, and demonstrate that incorporating diagnosis priors can adapt a pretrained model to specialized SVP tasks.