Is Heuristic Sampling Necessary in Training Deep Object Detectors?

Object Detectors Emerge in Deep Scene CNNs

With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these...

A System for Rapid Interactive Training of Object Detectors

Machine learning approaches have become the de-facto standard for creating object detectors (such as face and pedestrian detectors) which are robust to lighting, viewpoint, and pose. Generating su ciently large labeled data sets to support accurate training is often the most challenging problem. To address this, the active learning paradigm suggests interactive user input, creating an initial c...

Training Object Class Detectors from Eye Tracking Data

Training an object class detector typically requires a large set of images annotated with bounding-boxes, which is expensive and time consuming to create. We propose novel approach to annotate object locations which can substantially reduce annotation time. We first track the eye movements of annotators instructed to find the object and then propose a technique for deriving object bounding-boxe...

Revisiting knowledge transfer for training object class detectors

We propose to revisit knowledge transfer for training object detectors on target classes from weakly supervised training images, helped by a set of source classes with bounding-box annotations. We present a unified knowledge transfer framework based on training a single neural network multi-class object detector over all source classes, organized in a semantic hierarchy. This generates proposal...

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