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Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition

Paper recorded by Signals 4 on 2026-09-10 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-10 on arXiv · recorded by Signals 4 on 2026-09-11

Category: cs.CV · 计算机视觉 · first seen 2026-09-11

Abstract

3D skeleton-based gait emotion recognition faces high annotation costs, data scarcity, and poor generalization on heterogeneous data. This paper proposes SV-GCN, a single-stream multi-feature fusion framework with temporal invariance. We introduce intra-frame relative motion features to eliminate frame-rate sensitivity and embed heterogeneous cues at shallow layers, enabling early fusion without m

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#86 most recent of 237 cs.CV papers we have recorded · ↑ newer: Spectral Adapters for Segment Anything Model-based Segmentation of Col · ↓ older: Multimodal Taxonomic Conditioning for Generative Plankton Imagery
Cite this page: Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition: the #86 most recent of 237 cs.CV papers we have recorded (as of 2026-09-10). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/single-stream-multi-feature-fusion-with-temporal-robustness-for-gait-emotion-rec.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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