Search Results for author:
Found 5 papers, 2 papers with code
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).
Temporal 3D Shape Modeling for Video-Based Cloth-Changing Person Re-Identification
In this work, we propose "Temporal 3D ShapE Modeling for VCCRe-ID" (SEMI), a lightweight end-to-end framework that addresses these issues by learning human 3D shape representations.
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification
In this paper, we propose "Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification" (CVSL) to address these challenges.
Cloth-Changing Person Re-Identification
Contrastive Learning
+1
A Survey of Feature Types and Their Contributions for Camera Tampering Detection
We formulate tampering detection as a time series analysis problem, and design experiments to study the robustness and capability of various feature types.
