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Supplementary materials of GGMix for ICIP2025

SUPPLEMENTARY MATERIAL

SUPPLEMENTARY MATERIAL

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Point cloud is a prevalent format in representing 3D geometry. Regardless of the recent advances, unsupervised learning for 3D point clouds remains arduous for various tasks due to its unorganized and sparsely distributed nature. To address this challenge, we propose a geometry regularized point cloud autoencoder, aiming to preserve local geometry structure. In particular, based on the Mahalanobis distance, we propose a point cloud geometry metric counting the local statistics. It endeavors to maximize the posterior of the reconstruction conditioned on the input point cloud.

Supplementary Materials of "CURVE: CLIP-Utilized Reinforcement learning for Visual image Enhancement via Simple Image Processing" submitted to ICIP 2025

This document is the supplementary material for the paper submission: Test-time Vocabulary Adaptation for Language-driven Object Detection.

This is the supplementary material for the paper submission: Test-time Vocabulary Adaptation for Language-driven Object Detection.

This document serves as the supplementary material for the paper submission Test-time Vocabulary Adaptation for Language-driven Object Detection.

SUPPLEMENTARY MATERIAL for GGMix