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Found 8 papers, 1 papers with code
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification
In this work, we propose a Support Pair Active Learning (SPAL) framework to lower the manual labeling cost for large-scale person reidentification.
Unsupervised Clustering Active Learning for Person Re-identification
On the other hand, unsupervised re-id methods rely on unlabeled data to train models but performs poorly compared with supervised re-id methods.
Unsupervised Noisy Tracklet Person Re-identification
Extensive comparative experiments demonstrate that the proposed STL model surpasses significantly the state-of-the-art unsupervised learning and one-shot learning re-id methods on three large tracklet person re-id benchmarks.
Intra-Camera Supervised Person Re-Identification
Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data.
Part-based Multi-stream Model for Vehicle Searching
We can easily measure the similarity of two vehicle images by computing the Euclidean distance of the features from FC layer.
Intra-Camera Supervised Person Re-Identification: A New Benchmark
Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process therefore leading to poor scalability in practical re-id applications.
Unsupervised Tracklet Person Re-Identification
We formulate an Unsupervised Tracklet Association Learning (UTAL) framework.
Ranked #1 on
Person Re-Identification
on DukeTracklet
Unsupervised Person Re-identification by Deep Learning Tracklet Association
Mostexistingpersonre-identification(re-id)methods relyon supervised model learning on per-camera-pair manually labelled pairwise training data.
Ranked #2 on
Person Re-Identification
on DukeTracklet
