The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2511.18031v1 Announce Type: cross Abstract: Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that i...
arXiv:2605. 07821v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models.
arXiv:2606. 09245v1 Announce Type: cross Abstract: Few-shot object detection has gained widely attention in recent years.
The paper presents a generative framework that estimates category-level 6D pose and 3D size of objects from a single RGB image, using score-based diffusion models to produce a multi-hypothesis pose distribution. It replaces costly likelihood pruning with a Mean Shift approach to isolate the mode as the final pose estimate, achieving state-of-the-art results on the REAL275 benchmark. The method also decouples detection from pose estimation, enabling robust zero-shot generalisation on the Wild6D dataset and extending naturally to video sequences by propagating the pose distribution over time.
arXiv:2601.15829v3 Announce Type: replace Abstract: Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-sca...
ORSIFlow is a saliency‑guided rectified flow framework for optical remote sensing salient object detection that reframes the task as deterministic latent flow generation. It uses a frozen variational autoencoder to produce saliency masks in a compact latent space, allowing efficient inference with few steps. The method incorporates a Salient Feature Discriminator for global semantic discrimination and a Salient Feature Calibrator for precise boundary refinement, achieving state‑of‑the‑art performance on multiple public benchmarks.