3 DAILY ICCV Wednesday by Fatma Guney In our group, we focus on computer vision problems related to autonomous driving, hence the name autonomous vision. In the last few years, we worked on future prediction; predicting the next frames in a video or predicting the future trajectories of agents in the scene. Recently, we also started looking into the action part, which is learning to act based on perception and prediction input. After experiencing the difficulties of behavior learning first-hand, I better understand the requirements of robotics from computer vision algorithms, mainly efficiency. In this ICCV, we present one of the smallest and fastest trajectory prediction algorithms: ADAPT. I am currently betting on object-level reasoning, ideally without using any labels. We have another paper on unsupervised object discovery: When we also reason about 3D geometry, unsupervised segmentation becomes a lot more accurate! Lastly, we present “RbA: Rejected by All” on segmenting chickens on the road, a.k.a. out-ofdistribution objects. This has been a largely ignored problem until recently, we need to start thinking about those“corner cases”. Our 3 papers: ❖ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation ❖Multi-Object Discovery by Low-Dimensional Object Motion ❖RbA: Segmenting Unknown Regions Rejected by All
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