arXiv Computer Vision
DISTA-Net++: Rethinking Infrared Small Target Unmixing Beyond Sub-Pixel Separation
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arXiv AI
Jul 7
LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection
arXiv:2607. 04603v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing.
arXiv Computer Vision
Oct 22
Seeing through Denoising: Frequency-guided One-step Denoising for Infrared Small Target Detection
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 AI
Jul 8
NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation
arXiv:2607. 05955v1 Announce Type: cross Abstract: Interactive 3D segmentation aims to extract object masks in point clouds with minimal user clicks.
arXiv Machine Learning
Sep 30
TomoTransformer: Towards a Foundation Model for CT Reconstruction
arXiv:2609.37605v1 Announce Type: cross Abstract: Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-project...
arXiv Computer Vision
Aug 25
Semantic Reconstruction and 3-D Detection via Learned Multi-Pair Fusion in RF Imaging
arXiv:2608.23249v1 Announce Type: new Abstract: We consider a multistatic radio-frequency imaging problem with anisotropy, in which the reflection from a point depends on the positions of the transmi...
arXiv Computer Vision
Sep 3
SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging
SlowFast‑SCI introduces a dual‑speed deep‑unfolding framework for spectral compressive imaging that combines a slow, pre‑trained backbone with a fast, test‑time adaptation stage. The slow phase distills a priors‑based model into a compact fast‑unfolding network, while the fast phase embeds lightweight modules that self‑supervise at test time without retraining the backbone. This design yields significant reductions in parameters and FLOPs, improves out‑of‑distribution PSNR by up to 5.79 dB, and accelerates adaptation four‑fold, all while remaining modular enough to integrate with any existing deep‑unfolding system.