Multi-Object Tracking (MOT) is an important task in computer vision. Most research works on MOT focus on tracking multiple objects of a single category, such as humans. However, many applications, such as wildlife surveillance and autonomous driving, require multi-object trackers to track multiple categories of objects. In this work, the extension of state-of-the-art one-shot MOT methods for multi-class tracking is shown to be non-trivial, yielding deteriorated tracking performance due to the complexity of balancing the jointly learned detection and association tasks. To tackle this limitation, a simple improvement to FairMOT is proposed, which replaces the detection head with the decoupled head from YOLOX. The simplified multi-class detection framework yields improved detection and tracking results on the BDD100K MOT validation dataset, yielding an mHOTA of 25.3% compared to 19.7%.

Social cognition is the study of how people interact with each other in a social situation. An effective interaction would require higher degree of cognitive involvement between the participants and consequently, an enhanced synchrony between their neural mechanisms. In this study, twelve pairs of subjects interacted with each other via a cognitively engaging experimental paradigm in which they either competed or cooperated with each other for performing a task. While they were performing the task we incorporated electroencephalographic (EEG) hyperscanning techniques by simultaneously recording the EEG activities of the interacting subjects. We quantified these interactions by computing the inter-brain synchrony (IBS) and studied the changes in IBS under different experimental conditions. We found that the inter-brain synchrony between the subjects was significantly higher when they cooperated with each other as compared to the competitive scenario. Furthermore, we found that IBS was significantly enhanced when the subjects were physically separated i.e. they cooperated via an intranet network. In this work, we have demonstrated how EEG hyperscanning technique can be employed to study inter-brain synchronization under different conditions.

The WEBSOM methodology, proven effective for building very large text archives, includes a method that extracts labels for each document cluster assigned to nodes in the map. However, the WEBSOM method needs to retrieve all the words of all the documents associated to each node. Since maps may have more than 100,000 nodes and since the archive may contain up to seven million documents, the WEBSOM methodology needs a faster alternative method for keyword selection. Presented here is such an alternative method that is able to quickly deduce meaningful labels per node in the map. It does this just by analyzing the relative weight distribution of the SOM weight vectors and by taking advantage of some characteristics of the random projection method used in dimensionality reduction. The effectiveness of this technique is demonstrated on news document collections.