This HDR manuscript presents a summary of my research activities after my PhD
in 2010. It covers my work in computer vision from the postdoctoral position at
Inria Paris to my present researcher position at Inria Grenoble.

Understanding visual data automatically—one of the main challenges in computer
vision—is having a significant impact in many practical applications, and this
phenomenon can only increase with the continuous rise in digital image and
video content being generated. My work presented here focuses on a selection of
machine learning methods for computer vision problems. The core theme of these
methods is the extraction of priors as additional cues for recognition when
only partially-supervised data is available. Such partially-supervised data
includes cases where only weak annotations are available, e.g., image or video
labels describing the objects in a scene, instead of pixel-wise labels for
segmenting objects. It also includes scenarios where data is semi-supervised,
e.g., the problem of tracking objects in a video sequence when they are
annotated only in the first frame. A third example of partially-supervised data
is the case of incremental learning, where an existing model is updated with
new training data, in the absence of the original annotations used to train the
initial model. In addition to discussing approaches to handle all these
scenarios, which lack full annotations, we will also demonstrate the importance
of priors for a few fully-supervised recognition problems.