  The primary goal of this paper is to localize objects in a group of
semantically similar images jointly, also known as the object co-localization
problem. Most related existing works are essentially weakly-supervised, relying
prominently on the neighboring images' weak-supervision. Although weak
supervision is beneficial, it is not entirely reliable, for the results are
quite sensitive to the neighboring images considered. In this paper, we combine
it with a self-awareness phenomenon to mitigate this issue. By self-awareness
here, we refer to the solution derived from the image itself in the form of
saliency cue, which can also be unreliable if applied alone. Nevertheless,
combining these two paradigms together can lead to a better co-localization
ability. Specifically, we introduce a dynamic mediator that adaptively strikes
a proper balance between the two static solutions to provide an optimal
solution. Therefore, we call this method \textit{ASOC}: Adaptive Self-aware
Object Co-localization. We perform exhaustive experiments on several benchmark
datasets and validate that weak-supervision supplemented with self-awareness
has superior performance outperforming several compared competing methods.
