Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.
( Image credit: Tensorflow Object Detection API )
We demonstrate that our single shot MC dropout approximation resembles the point estimate and the uncertainty estimate of the predictive distribution that is achieved with an MC approach, while being fast enough for real-time deployments of BDNNs.
Specifically, given a labeled training set and a model, we aim to estimate the model accuracy on unlabeled test datasets.
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
We find that detection accuracy increases drastically when we focus on the Gray Matter (GM) and White Matter (WM) regions from the MR images instead of using whole MR images.
Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different--but we envision our general-purpose error consistency analysis to serve as a fruitful tool for quantifying future progress.
The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.
For face recognition, a 1000 frame RGB video, featuring many faces together, has been used for benchmarking of the proposed approach.
Recent works demonstrate the texture bias in Convolutional Neural Networks (CNNs), conflicting with early works claiming that networks identify objects using shape.
To achieve this aim, a previous study has proven an upper bound of the target-domain risk, and the open set difference, as an important term in the upper bound, is used to measure the risk on unknown target data.