Boosting is a method for finding a highly accurate hypothesis by linearly combining many ``weak" hypotheses, each of which may be only moderately accurate.
We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset.
The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object.
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner.