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 …

Current rates of species loss triggered numerous attempts to protect and conserve
biodiversity. Species conservation, however, requires species identification skills, a …

Abstract We design a Kernelized Few-shot Object Detector by leveraging kernelized
matrices computed over multiple proposal regions, which yield expressive non-linear …

Learning new concepts from a few of samples is a standard challenge in computer vision.
The main directions to improve the learning ability of few-shot training models include (i) a …

Minimizing the discrepancy of feature distributions between different domains is one of the
most promising directions in unsupervised domain adaptation. From the perspective of …

By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets)
effectively learn from low-level to high-level features and discriminative representations …

Many few-shot learning models focus on recognising images. In contrast, we tackle a
challenging task of few-shot action recognition from videos. We build on a C3D encoder for …

Traditional recognition methods typically require large, artificially-balanced training classes,
while few-shot learning methods are tested on artificially small ones. In contrast to both …

Graph contrastive learning attracts/disperses node representations for similar/dissimilar
node pairs under some notion of similarity. It may be combined with a low-dimensional …

Place Recognition enables the estimation of a globally consistent map and trajectory by
providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This …