NotAfraidOfWong

Problem definition The talk was given by Prof. Trevor Darrell and he starts by comparing Machine Learning models to the mind of a human being. He questions why ImageNet can perform very well in static images, but poorly in videos even though a video is just a sequence of images? Possible reasons to this question is dataset bias, which prevents the model to adapt to different environment (such as lower resolution than trained), alterations in the image (such as motion blur) and so forth. Prof. Trevor Darrell is concerned that machine learning models are trained only to do specific tasks and he goes on to talk about his vision of “Beyond Supervised AI”. There are three themes: Adaptation How can we build models that can work across domains (or a change in environment)? Exploration How can we teach a model to explore instead of providing it very specific rewards/goals? Explanation Can we design models to tell us why they think the way they do? Algorithm, Results and Discu...

A 2019 Guide to Object Detection 1) Introduction Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. The objects can generally be identified from either pictures or video feeds. Object detection has been applied widely in video surveillance , self-driving cars, and object/people tracking. In this piece, we’ll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well. Object detection usually involves two processes; classifying and object’s type , and then drawing a bounding box . Common object detection model architectures: R-CNN Fast R-CNN Faster R-CNN Mask R-CNN (todo) SSD (Single Shot MultiBox Defender) YOLO (You Only Look Once) Objects as Points (todo) Data Augmentation Strategies for Object Detection (todo) 2.1) Why not use standard CNN? The major reason why you cannot proceed with this ...

CNN Performance Factors It is very hard to have a fair comparison among different object detectors. There is no straight answer on which model is the best. For real-life applications, we make choices to balance accuracy and speed. Besides the detector types, we need to aware of other choices that impact the performance: 1) Feature Extractors (VGG16, ResNet, Inception, MobileNet) Feature extraction is a process of dimensionality reduction by which an initial set of raw data is reduced to more manageable groups for processing. Feature extraction is the name for methods that select and/or combine variables into features, effectively reducing the amount of data that must be processed, while still accurately and completely describing the original data set. A CNN is composed of two basic parts of feature extraction and classification . Feature extraction includes several convolution layers followed by max-pooling and an activation function. The classifier usually consists of fully c...