SIFT meets CNN: A decade survey of instance retrieval
In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …
Source-free unsupervised domain adaptation: Current research and future directions
In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …
Fine-tuning CNN image retrieval with no human annotation
Image descriptors based on activations of Convolutional Neural Networks (CNNs) have
become dominant in image retrieval due to their discriminative power, compactness of …
become dominant in image retrieval due to their discriminative power, compactness of …
Generalizing a person retrieval model hetero-and homogeneously
Person re-identification (re-ID) poses unique challenges for unsupervised domain
adaptation (UDA) in that classes in the source and target sets (domains) are entirely different …
adaptation (UDA) in that classes in the source and target sets (domains) are entirely different …
Re-ranking person re-identification with k-reciprocal encoding
When considering person re-identification (re-ID) as a retrieval process, re-ranking is a
critical step to improve its accuracy. Yet in the re-ID community, limited effort has been …
critical step to improve its accuracy. Yet in the re-ID community, limited effort has been …
Pedestrian alignment network for large-scale person re-identification
Person re-identification (re-ID) is mostly viewed as an image retrieval problem. This task
aims to search a query person in a large image pool. In practice, person re-ID usually adopts …
aims to search a query person in a large image pool. In practice, person re-ID usually adopts …
Deep image retrieval: Learning global representations for image search
We propose a novel approach for instance-level image retrieval. It produces a global and
compact fixed-length representation for each image by aggregating many region-wise …
compact fixed-length representation for each image by aggregating many region-wise …
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN) has been
shown to provide effective descriptors for image search, outperforming pre-CNN features as …
shown to provide effective descriptors for image search, outperforming pre-CNN features as …