Class Specific Anchoring Proposal for 3D Object Detection

The detection of specific objects of interest within 2D images is a well-studied problem. However, detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in 3D, such as in the case of autonomous driving vehicles. Therefore, accurately and efficiently detecting objects in the three-dimensional setting is becoming increasingly relevant to a wide range of industrial applications, and thus is progressively attracting the attention of researchers. Building systems to detect objects in 3 dimensions is a challenging task though, because it relies on the multi-modal fusion of data derived from different sources. In this study, we study the effects of anchoring using the current state-of-the-art 3D object detector.

Humayun Irshad is currently the Lead Scientist of Machine Learning at Figure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. He has expertise in developing machine learning, more specifically deep learning frameworks for various applications like object detection, segmentation and classification in fields ranging from medical, retail, self-driving car, satellite, fashion, etc. Now a days, he is building Active Learning frameworks for selection of training data from labeled or unlabeled dataset to build model to avoid over-training and dealing corner cases. He has 3 years PostDoc experience at Harvard Medical School where he developed machine learning and deep learning frameworks for Computer Aided Diagnosis system including region of interest detection, nuclei and gland detection, segmentation and classification in 2D and 3D medical images. He got a PhD in Computer Science from University of Grenoble France.