Visual localization is the task of estimating a 6-DoF camera pose of a query image within a provided 3D reference map.
The time-series anomaly detection is one of the most fundamental tasks for time-series.
Furthermore, as the relationship between context and motion is important in order to identify the anomalies in complex and diverse scenes, we propose a Context--Motion Interrelation Module (CoMo), which models the relationship between the appearance of the surroundings and motion, rather than utilizing only temporal dependencies or motion information.
In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames.
Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects, especially in crowded scenes.
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos.
The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0. 647.
It is known that using languages with similar language structures is effective for cross lingual transfer learning (Pires et al., 2019).
While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications.
Before finding the best matches for the query frame pixels, the optimal matches for the reference frame pixels are first considered to prevent each reference frame pixel from being overly referenced.
We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable.
The GW method is a many-body approach capable of providing quasiparticle bands for realistic systems spanning physics, chemistry, and materials science.