🕸️ Graph Learning

🎞️ ECCV2024 · 4 paper notes

📌 Same area in other venues: 📷 CVPR2026 (8) · 🔬 ICLR2026 (118) · 💬 ACL2026 (24) · 🧪 ICML2026 (35) · 🤖 AAAI2026 (37) · 🧠 NeurIPS2025 (54)

Confidence Self-Calibration for Multi-Label Class-Incremental Learning

To address the overconfident predictions and false-positive errors caused by partial labels in Multi-Label Class-Incremental Learning (MLCIL), a Confidence Self-Calibration (CSC) framework is proposed. It calibrates label relationships using a Class-Incremental Graph Convolutional Network (CI-GCN) and calibrates confidence via max-entropy regularization, significantly outperforming SOTA methods on MS-COCO and VOC.

GKGNet: Group K-Nearest Neighbor Based Graph Convolutional Network for Multi-Label Image Recognition

Proposes GKGNet, the first fully graph convolutional multi-label recognition model, which dynamically constructs graph structures between labels and image regions utilizing a Group KNN mechanism, achieving SOTA performance on MS-COCO and VOC2007 with lower computational cost.

SENC: Handling Self-collision in Neural Cloth Simulation

This paper proposes SENC, which effectively addresses the cloth self-collision problem in self-supervised neural cloth simulation for the first time, using a self-collision loss based on Global Intersection Analysis (GIA) and a self-collision-aware graph neural network.

Synchronous Diffusion for Unsupervised Smooth Non-Rigid 3D Shape Matching

A synchronous diffusion regularization method is proposed for unsupervised non-rigid 3D shape matching. The core idea is that "synchronously diffusing the same function on two shapes should yield consistent outputs." Through this simple yet efficient regularization, the matching smoothness of existing deep functional map methods is significantly improved, achieving SOTA performance on several datasets including FAUST, SCAPE, and TOPKIDS.