  Weakly supervised video object localization (WSVOL) allows locating object in
videos using only global video tags such as object class. State-of-art methods
rely on multiple independent stages, where initial spatio-temporal proposals
are generated using visual and motion cues, then prominent objects are
identified and refined. Localization is done by solving an optimization problem
over one or more videos, and video tags are typically used for video
clustering. This requires a model per-video or per-class making for costly
inference. Moreover, localized regions are not necessary discriminant because
of unsupervised motion methods like optical flow, or because video tags are
discarded from optimization. In this paper, we leverage the successful class
activation mapping (CAM) methods, designed for WSOL based on still images. A
new Temporal CAM (TCAM) method is introduced to train a discriminant deep
learning (DL) model to exploit spatio-temporal information in videos, using an
aggregation mechanism, called CAM-Temporal Max Pooling (CAM-TMP), over
consecutive CAMs. In particular, activations of regions of interest (ROIs) are
collected from CAMs produced by a pretrained CNN classifier to build pixel-wise
pseudo-labels for training the DL model. In addition, a global unsupervised
size constraint, and local constraint such as CRF are used to yield more
accurate CAMs. Inference over single independent frames allows parallel
processing of a clip of frames, and real-time localization. Extensive
experiments on two challenging YouTube-Objects datasets for unconstrained
videos, indicate that CAM methods (trained on independent frames) can yield
decent localization accuracy. Our proposed TCAM method achieves a new
state-of-art in WSVOL accuracy, and visual results suggest that it can be
adapted for subsequent tasks like visual object tracking and detection. Code is
publicly available.
