We propose to address the problem of few-shot classification by meta-learning" what to
observe" and" where to attend" in a relational perspective. Our method leverages relational …

Object class detection, also known as category-level object detection, has become one of
the most focused areas in computer vision in the new century. This article attempts to …

Statistical-learning-based object detection is an important topic in computer vision. It learns
visual representation from annotated exemplars to identify semantic defined objects in …

Inferior temporal (IT) cortex in human and nonhuman primates serves visual object
recognition. Computational object-vision models, although continually improving, do not yet …

This paper presents a variational-based approach for fusing hyperspectral and multispectral
images. The fusion problem is formulated as an inverse problem whose solution is the target …

Despite advances in global image representation, existing image retrieval approaches
rarely consider geometric structure during the global retrieval stage. In this work, we revisit …

Co-saliency is used to discover the common saliency on the multiple images, which is a
relatively underexplored area. In this paper, we introduce a new cluster-based algorithm for …

Change detection in heterogeneous multitemporal satellite images is an emerging and
challenging topic in remote sensing. In particular, one of the main challenges is to tackle the …

This paper presents a Bayesian fusion technique for remotely sensed multi-band images.
The observed images are related to the high spectral and high spatial resolution image to be …

Cosegmentation is typically defined as the task of jointly segmenting “something similar” in a
given set of images. Existing methods are too generic and so far have not demonstrated …