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Paper#Computer Vision, Deep Learning, Correspondence Learning🔬 ResearchAnalyzed: Jan 3, 2026 18:46

SC-Net: Improved Correspondence Learning with Context

Published:Dec 29, 2025 13:56
1 min read
ArXiv

Analysis

This paper introduces SC-Net, a novel network for two-view correspondence learning. It addresses limitations of existing CNN-based methods by incorporating spatial and cross-channel context. The proposed modules (AFR, BFA, PAR) aim to improve position-awareness, robustness, and motion field refinement, leading to better performance in relative pose estimation and outlier removal. The availability of source code is a positive aspect.
Reference

“SC-Net outperforms state-of-the-art methods in relative pose estimation and outlier removal tasks on YFCC100M and SUN3D datasets.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Dec 25, 2025 11:43

Causal-Driven Attribution (CDA): Estimating Channel Influence Without User-Level Data

Published:Dec 25, 2025 05:00
1 min read
ArXiv Stats ML

Analysis

This paper introduces a novel approach to marketing attribution called Causal-Driven Attribution (CDA). CDA addresses the growing challenge of data privacy by estimating channel influence using only aggregated impression-level data, eliminating the need for user-level tracking. The framework combines temporal causal discovery with causal effect estimation, offering a privacy-preserving and interpretable alternative to traditional path-based models. The results on synthetic data are promising, showing good accuracy even with imperfect causal graph prediction. This research is significant because it provides a potential solution for marketers to understand channel effectiveness in a privacy-conscious world. Further validation with real-world data is needed.
Reference

“CDA captures cross-channel interdependencies while providing interpretable, privacy-preserving attribution insights, offering a scalable and future-proof alternative to traditional path-based models.”

PermalinkArXiv Stats ML