Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.
Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rel...
Oracle bone script (OBS), one of the earliest Chinese writing systems, plays an important role in the study of Chinese etymology. Traditional decipherment relies heavily on domain experts who analyze...
Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in complex anatomies. Altho...
Zero-shot anomaly detection aims to localize anomalies without target-domain samples. Existing CLIP-based methods suffer from coarse anomaly maps and limited semantic prompts. We propose PSMP-CLIP, in...
Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accuracy, they rely heavily on long-range spatial fea...
Category-level object pose estimation seeks to recover a similarity transform $(R,t,s)$ for unseen instances without instance-specific CAD models. Most competitive methods are correspondence-based: pr...
State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Dual...
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:2609.13232v1 Announce Type: new Abstract: A robot pruning trees needs two facts per pixel: whether it belongs to a tree, and its distance. Both are usually obtained via task heads attached to a...
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:2609.13706v1 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:2609.13617v1 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...
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:2603.13044v2 Announce Type: replace-cross Abstract: Medical image segmentation (MIS) is a fundamental component of computer-assisted diagnosis and clinical decision support. Over the past decad...
The paper introduces AdaConRed, a label‑free post‑conformal decision rule that transforms ambiguous conformal prediction sets into single class assignments for medical image classification. It employs a five‑stage pipeline—vision‑language generative augmentation, a frozen DermFoundation encoder, a lightweight MLP classifier, an entropy‑modulated margin‑aware nonconformity score, and a reassignment step for transitional samples—to improve accuracy on OSCC and ISIC benchmarks. Results show notable gains in malignant class accuracy while maintaining overall performance.
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:2609.15659v1 Announce Type: cross Abstract: Native 3D generators turn one image into a single mesh. TRELLIS.2 and its peers deliver high-fidelity non-watertight geometry with materials, but the...
The paper introduces CORAL, a multimodal framework that combines spatial grounding and concept-level supervision for medical report generation. CORAL uses a prompt-driven segmentation model to localize lesions and a Concept Bottleneck module to predict multi-class clinical attributes, feeding these textual concept tokens and mask-modulated visual features into a multimodal large language model. Experiments on BUS-CoT and IU X-ray datasets show that CORAL improves diagnostic accuracy, concept consistency, and report quality compared to existing general-purpose and medical MLLMs.
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:2609.14278v1 Announce Type: cross Abstract: Accurate sperm detection is essential for computer-assisted semen analysis, yet it remains challenging in microscopic images due to dense distributio...