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.
MOSAIC is an object‑centric framework that adapts pretrained diffusion Transformers for image restoration by learning compact structural conditions. It organizes degraded images into structural slots capturing object‑level and layout‑level evidence, integrates these across resolutions, and retrieves recurring priors from a shared prototype memory. Injecting these representations into selected layers of a frozen diffusion Transformer via lightweight residual modulation preserves the pretrained generative prior, yielding improved remote sensing image super‑resolution performance on UCMerced, AID, and RSSCN7 datasets.
The paper introduces DAFIC, a geometry‑guided framework for few‑shot image classification that leverages monocular depth information. DAFIC first learns geometry‑aware token representations via RGB‑D self‑supervised pre‑training, then refines patch representations using a Depth‑guided Geometric Adaptation module that incorporates depth‑derived geometry while suppressing background interference. Finally, Spatial Bidirectional Patch Matching performs dense bidirectional matching on the refined features, reducing sensitivity to spurious correspondences and background clutter. Experiments on four benchmarks demonstrate that DAFIC outperforms existing methods, with ablation and robustness analyses confirming the benefits of coupling geometry‑conditioned episodic adaptation with bidirectional matching.
arXiv:2606. 30215v1 Announce Type: cross Abstract: RGB-T detectors leverage the complementary strengths of visible and thermal infrared modalities, achieving robust performance under challenging conditions.
The paper introduces DiffIRSTD, a generative framework that treats infrared small target detection as a background reconstruction problem. By interpreting small targets as noise-like signals, a diffusion-based model recovers a clean, target-free background, and the residual highlights candidate targets. A one-step denoising strategy and a frequency-guided perception loss enable efficient, high-quality reconstruction while remaining plug‑and‑play for existing detectors, achieving state‑of‑the‑art results on three IRSTD benchmarks.
arXiv:2512.15971v2 Announce Type: replace Abstract: Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception u...
arXiv:2610.33518v1 Announce Type: new Abstract: Few-shot remote sensing scene classification (FSRSSC) aims to recognize novel scene categories from only a few labeled images. Most FSRSSC methods summ...