Scene-graph generation involves creating a structural representation of the relationships between objects in a scene by predicting subject-object-relation triplets from input data. Existing methods show poor performance in detecting triplets outside of a predefined set, primarily due to their reliance on dependent feature learning. To address this issue we propose DDS– a decoupled dynamic scene-graph generation network- that consists of two independent branches that can disentangle extracted features. The key innovation of the current paper is the decoupling of the features representing the relationships from those of the objects, which enables the detection of novel object-relationship combinations. The DDS model is evaluated on three datasets and outperforms previous methods by a significant margin, especially in detecting previously unseen triplets.

We present a real-time body orientation estimation in a micro-Unmanned Air Vehicle video stream. This work is part ofafully autonomous UAVsystem which can maneuver to face a single individual in challenging outdoor environments. Our body orientation estimation consists of the following steps: (a) obtaining a set ofvisual appearance models for each body orientation, where each model is tagged with a set of scene information (obtained from sensors), (b) exploiting the mutual information of on-board sensors using latent-dynamic conditional random fields (WCRF), (c) Characterizing each visual appearance model with the most discriminative sensor information, (d) fast estimation ofbody orientation during the test flights given theWCRF parameters and the corresponding sensor readings. The key aspects of our approach is to add sparsity to the sensor readings with latent variables followed by long range dependency analysis. Experimental results obtained over real-time video streams demonstrate a significant improvement in both speed (l5-fps) and accuracy (72%) compared to the state of the art techniques that only rely on visual data. Video demonstration ofour autonomous flights (both from ground view and aerial view) are included in the supplementary material.