However, these works do not explicitly reduce the data bias across different house scenes.
Ranked #16 on Vision and Language Navigation on VLN Challenge
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions.
Recently, the notions of subjective constraint monotonicity, epistemic splitting, and foundedness have been introduced for epistemic logic programs, with the aim to use them as main criteria respectively intuitions to compare different answer set semantics proposed in the literature on how they comply with these intuitions.
To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras.
In this paper, we address this problem by training relational context-aware agents which learn the actions to localize the target person from the gallery of whole scene images.
In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.
Ranked #1 on NLP based Person Retrival on CUHK-PEDES (R@1 metric)
Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data.