This thesis gives an overview of my research since my arrival in December 2005 as a postdoctoral fellow at the in the LEAR team at INRIA Rhone-Alpes. After a general introduction in Chapter 1, the contributions are presented in chapters 2–4 along three themes. In each chapter we describe the contributions, their relation to related work, and highlight two contributions with more detail. Chapter 2 is concerned with contributions related to the Fisher vector representation. We highlight an extension of the representation based on modeling dependencies among local descriptors (Cinbis et al., 2012, 2016a). The second highlight is on an approximate normalization scheme which speeds-up applications for object and action localization (Oneata et al., 2014b). In Chapter 3 we consider the contributions related to metric learning. The first contribution we highlight is a nearest-neighbor based image annotation method that learns weights over neighbors, and effectively determines the number of neighbors to use (Guillaumin et al., 2009a). The second contribution we highlight is an image classification method based on metric learning for the nearest class mean classifier that can efficiently generalize to new classes (Mensink et al., 2012, 2013b). The third set of contributions, presented in Chapter 4, is related to learning visual recognition models from incomplete supervision. The first highlighted contribution is an interactive image annotation method that exploits dependencies across different image labels, to improve predictions and to identify the most informative user input (Mensink et al., 2011, 2013a). The second highlighted contribution is a multi-fold multiple instance learning method for learning object localization models from training images where we only know if the object is present in the image or not (Cinbis et al., 2014, 2016b). Finally, Chapter 5 summarizes the contributions, and presents future research directions.