4 accepted papers
Keypoint estimation is a fundamental task in computer vision, but generally requires large-scale annotated data for training. Few-shot and unsupervised keypoint estimation are prevalent economical paradigms, but the former still requires annotations for extensive novel classes while the latter only…
The grounding accuracy of existing video captioners is still behind the expectation. The majority of existing methods perform grounded video captioning on sparse entity annotations, whereas the captioning accuracy often suffers from degenerated object appearances on the annotated area such as motion…
Attention supervision encourages grounded video description models (GVDMs) to focus on the related visual content when generating words. Thus, it improves the description performance of GVDMs. However, existing GVDMs often fail to focus on small but informative regions because these regions are cons…
Most instance search systems are based on modeling local features. It remains a challenge to apply deep learning techniques into this task because of the asymmetrical similarity between the query region and dataset images. In this paper, we propose ALADDIN, A Locality Aligned Deep moDel for INstance…