Detection, segmentation, depth and recognition research, plus the vision backbones that keep displacing the last generation.
arXiv:2604.03212v4 Announce Type: replace Abstract: Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across sea...
arXiv:2608.20713v1 Announce Type: new Abstract: Generative AI can now produce highly realistic images, yet current models still exhibit subtle but critical defects that undermine their reliability. W...
arXiv:2608.20942v1 Announce Type: new Abstract: Motivated by the classical Chan-Vese model and the ability of deep priors to capture complex spatial structures, we develop a segmentation model that l...
arXiv:2608.18012v2 Announce Type: replace Abstract: One of the most common reasons for anterior cruciate ligament (ACL) reconstruction failure is femoral tunnel malpositioning (ACL footprint center a...
The paper surveys multimodal speculative decoding, examining whether diffusion-based block‑parallel generative drafting—successful in text‑only LLMs—can be applied to Vision‑Language, Video‑Language, Audio, and Vision‑Language‑Action models. It introduces a taxonomy separating drafter‑side parallelism from other design choices, and presents an empirical comparison across benchmarks such as OCR, VQA, visual reasoning, and image captioning. The study highlights current limitations, outlines open challenges, and suggests future research directions for multimodal speculative decoding.
arXiv:2608.11645v2 Announce Type: replace Abstract: Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame...
arXiv:2608.21140v1 Announce Type: cross Abstract: Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report genera...
arXiv:2608.21300v1 Announce Type: new Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot se...
arXiv:2608.21099v1 Announce Type: cross Abstract: Multi-modal object detection is essential for robust scene understanding in challenging conditions, including low-light and adverse environments. Rec...
arXiv:2404.09431v3 Announce Type: replace Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
arXiv:2608.21098v1 Announce Type: new Abstract: Fusing prior knowledge with data-driven learning is attractive where data is scarce, yet no controlled account says when it helps, is redundant, or har...
arXiv:2608.21066v1 Announce Type: new Abstract: Deploying autonomous systems in safety-critical domains demands guaranteed robustness against physically plausible geometric perturbations rather than...
arXiv:2608.20754v1 Announce Type: new Abstract: Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a mism...
arXiv:2608.20944v1 Announce Type: new Abstract: Multimodal object detection in remote sensing faces challenges due to semantic heterogeneity and modality-specific noise interference. To this end, we...
arXiv:2605.10894v2 Announce Type: replace Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner h...
The paper introduces OraRL, a reinforcement learning framework that leverages annotations as oracle rollouts to improve sample efficiency and scalability for video multimodal large language models (MLLMs). By decoupling advantage estimation and employing sign‑balanced pruning, OraRL achieves faster training and better performance across multiple video‑perception benchmarks compared to existing methods. The approach scales from 0.8B to 9B parameters and handles up to 100k prompts, delivering significant gains in temporal mIoU, tracking accuracy, segmentation, and spatial‑intelligence metrics.
arXiv:2505.18930v2 Announce Type: replace-cross Abstract: Early weed identification is crucial for effective management and control, and researchers, agronomists, and technology developers are increa...
arXiv:2608.20929v1 Announce Type: new Abstract: AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel s...
arXiv:2608.20740v1 Announce Type: new Abstract: State-of-the-art 3D reconstruction models, whether from visual, range, or both, tend to underperform on thin objects. This is partially due to the smal...