6 accepted papers
In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed network to predict the action of each frame with the neighboring frames. However, this would result in ambiguity when estimating similar actions, such a…
It is difficult to construct a data collection including all possible combinations of human actions and interacting objects due to the combinatorial nature of human-object interactions (HOI). In this work, we aim to develop a transferable HOI detector for unseen interactions. Existing HOI detectors…
In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories. Previous HOI studies are mainly conducted in the regime of limit object categories (e.g., 80 categories). Their solutions may face new difficulties in both object detection and int…
In this work, we address the challenging task of referring segmentation. The query expression in referring segmentation typically indicates the target object by describing its relationship with others. Therefore, to find the target one among all instances in the image, the model must have a holistic…
We aim to detect human interactions with novel objects through zero-shot learning. Different from previous works, we allow unseen object categories by using its semantic word embedding. To do so, we design a human-object region proposal network specifically for the human-object interaction detection…
In this paper, we propose a semi-supervised approach for representative selection, which finds a small set of representatives that can well summarize a large data collection. Given labeled source data and big unlabeled target data, we aim to find representatives in the target data, which can not onl…