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Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models
In the midst of the rapid integration of artificial intelligence (AI) into real world applications, one pressing challenge we confront is the phenomenon of model drift, wherein the performance of AI models gradually degrades over time, compromising their effectiveness in real-world, dynamic environments.
Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification
Our ASGL framework improves Re-ID performance under clothing variations by learning clothing-invariant gait cues using a Spatial-Temporal Graph Attention Network (ST-GAT).
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