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Found 5 papers, 1 papers with code
Going Deeper into Semi-supervised Person Re-identification
To reduce the need for labeled data, we focus on a semi-supervised approach that requires only a subset of the training data to be labeled.
Semi-supervised Keypoint Localization
Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset.
Keypoint-Aligned Embeddings for Image Retrieval and Re-identification
Learning embeddings that are invariant to the pose of the object is crucial in visual image retrieval and re-identification.
Learning landmark guided embeddings for animal re-identification
Our method outperforms the same model without body landmarks input by 26% and 18% on the synthetic and the real datasets respectively.
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings
We present a novel system for visual re-identification based on unique natural markings that is robust to occlusions, viewpoint and illumination changes.
