CORTEXA
← Browse
arxiveess.IVcs.CV2026-07-05

MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement

Dineth Perera, Thaariq Firdous, Oshadha Samarakoon, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath

Binary change detection in remote sensing requires both complete changed-region localization and accurate boundary delineation. We present MambaRefine-CD, a region-boundary temporal refinement framework built on a shared MambaVision encoder. The proposed D-RBI module constructs temporal evidence from paired features, absolute differences, and signed differences, then separates it into region and Sobel-conditioned boundary streams. Region features are enhanced with CRAM-lite and decoded by an adaptive receptive-field FPN, while the finest boundary stream guides a bounded residual refinement of the coarse prediction. Experiments on DSIFN-CD and WHU-CD show strong changed-class F1 and IoU under verified evaluation settings, and ablations support the contribution of signed temporal evidence and the full region-boundary refinement pipeline.

View free PDFSource page

Related papers

arxivcs.CVcs.LGeess.IV2026-07-24

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement

Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor

Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis. This study presents a reproducible workflow for mapping farmland extent and visible boundaries…

View free PDFSource page
arxiveess.IVcs.CVcs.LG2026-07-31

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

Sebastian Doerrich, Daniel Würtinger, Francesco Di Salvo, Shyam Nandan Rai, Christian Ledig

Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates. Although PEFT successfully navigates limited data scenarios, it inherently…

View free PDFSource page
arxivcs.CVcs.AIeess.IV2026-07-24

Time-Reversed Imaging: A Multimodal Benchmark and Framework for Reconstructing Past Human-Environment Interactions

Jorge Bacca, Kebin Contreras, Luis Toscano-Palomino, Mauro Dalla Mura

We introduce time-reversed imaging, a new paradigm that infers what just happened in a scene from fading multimodal traces. Instead of extrapolating or interpolating video frames, our goal is to infer past human-environment interactions from residual physical imprints observable…

View free PDFSource page
arxivcs.CRcs.CVcs.SEeess.IV2026-07-31

A Biometric Sensor Network to Enable Real-Time Measurement of Individual Student Engagement in STEM Lecture Environments

Ahmed Elsayed

Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for real-time classroom use. This thesis proposes a novel $\textit{Biometric Sensor…

View free PDFSource page
arxiveess.IVcs.CVphysics.optics2026-07-24

The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing

Yuyuan Han, Jingwei Li, Long Qiu, Chong Wang, Wenxuan Hao, Jiangyu Han, et al.

Single-pixel sensing encodes a scene as a short sequence of coded measurements, and image-free methods infer the task directly from that sequence. Removing reconstruction does not remove the difficulty: it relocates it to the lift, the map from 1D measurements to a 2D representat…

View free PDFSource page