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Please refer to our supplementary materials as follows:

The supplementary_AniMake PDF includes additional results, and detailed explanation.

The demo video provides a demonstration along with sample results.

Facial Expression Recognition (FER) has achieved significant success in recent years due to the rise of deep learning. Meanwhile, latent semantic information is crucial for recognizing facial expressions with subtle differences. Inspired by inconsistencies in learning intensity across different layers of deep learning networks — where shallow-layer features lack generalization and task relevance compared to deep-layer features — we propose a novel Hierarchical Semantic Transfer (HST) method.

Facial Expression Recognition (FER) has achieved significant success in recent years due to the rise of deep learning. Meanwhile, latent semantic information is crucial for recognizing facial expressions with subtle differences. Inspired by inconsistencies in learning intensity across different layers of deep learning networks — where shallow-layer features lack generalization and task relevance compared to deep-layer features — we propose a novel Hierarchical Semantic Transfer (HST) method.

Supplementary of IMPROVING OPEN-WORLD CLASS-AGNOSTIC OBJECT DETECTORS VIA FEATURE DISTILLATION WITH STUDENT-AWARE ADAPTATION

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