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Found 5 papers, 3 papers with code
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification
Unlike mainstream approaches using global features for simultaneous multi-task learning of ReID and human parsing, or relying on semantic information for attention guidance, DROP argues that the inferior performance of the former is due to distinct granularity requirements for ReID and human parsing features.
Online Video Quality Enhancement with Spatial-Temporal Look-up Tables
To the best of our knowledge, we are the first to exploit the LUT structure to extract temporal information in video tasks.
Content-Adaptive Auto-Occlusion Network for Occluded Person Re-Identification
To address these challenges, we propose a novel Content-Adaptive Auto-Occlusion Network (CAAO), that is able to dynamically select the proper occlusion region of an image based on its content and the current training status.
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
To facilitate the research on this problem, a new benchmark dataset named LDV-WebRTC is constructed based on a real-world online streaming system.
Context-Aware Feature Learning for Noise Robust Person Search
For human-introduced noise, we propose a noise-discovery and noise-suppression training process for mislabeling robust person search.
