Deep neural networks (DNNs) achieve excellent performance on standard classification tasks. However, under image quality distortions such as blur and noise, classification accuracy becomes poor. In this work, we compare the performance of DNNs with human subjects on distorted images. We show that, although DNNs perform better than or on par with humans on good quality images, DNN performance is still much lower than human performance on distorted images. We additionally find that there is little correlation in errors between DNNs and human subjects. This could be an indication that the internal representation of images are different between DNNs and the human visual system. These comparisons with human performance could be used to guide future development of more robust DNNs.
Comparing the performance of machine learning systems with human subjects has attracted interest because it may give insights on how machine learning systems can be improved. Borji and Itti @cite_7 compare human classification accuracy and the accuracy of several machine learning algorithms on several datasets. The study considers images as well as line drawings and jumbled images. The study, however, does not test distorted images, and does not include deep learning algorithms.
@cite_7
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The performance of current state-of-the-art computer vision algorithms at image classification falls significantly short as compared to human abilities. To reduce this gap, it is important for the community to know what problems to solve, and not just how to solve them. Towards this goal, via the use of jumbled images, we strip apart two widely investigated aspects: local and global information in images, and identify the performance bottleneck. Interestingly, humans have been shown to reliably recognize jumbled images. The goal of our paper is to determine a functional model that mimics how humans recognize jumbled images i.e. exploit local information alone, and further evaluate if existing implementations of this computational model suffice to match human performance. Surprisingly, in our series of human studies and machine experiments, we find that a simple bag-of-words based majority-vote-like strategy is an accurate functional model of how humans recognize jumbled images. Moreover, a straightforward machine implementation of this model achieves accuracies similar to human subjects at classifying jumbled images. This indicates that perhaps existing machine vision techniques already leverage local information from images effectively, and future research efforts should be focused on more advanced modeling of global information. Several decades of research in computer and primate vision have resulted in many models (some specialized for one problem, others more general) and invaluable experimental data. Here, to help focus research efforts onto the hardest unsolved problems, and bridge computer and human vision, we define a battery of 5 tests that measure the gap between human and machine performances in several dimensions (generalization across scene categories, generalization from images to edge maps and line drawings, invariance to rotation and scaling, local global information with jumbled images, and object recognition performance). We measure model accuracy and the correlation between model and human error patterns. Experimenting over 7 datasets, where human data is available, and gauging 14 well-established models, we find that none fully resembles humans in all aspects, and we learn from each test which models and features are more promising in approaching humans in the tested dimension. Across all tests, we find that models based on local edge histograms consistently resemble humans more, while several scene statistics or "gist" models do perform well with both scenes and objects. While computer vision has long been inspired by human vision, we believe systematic efforts, such as this, will help better identify shortcomings of models and find new paths forward. We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system. Traffic signs are characterized by a wide variability in their visual appearance in real-world environments. For example, changes of illumination, varying weather conditions and partial occlusions impact the perception of road signs. In practice, a large number of different sign classes needs to be recognized with very high accuracy. Traffic signs have been designed to be easily readable for humans, who perform very well at this task. For computer systems, however, classifying traffic signs still seems to pose a challenging pattern recognition problem. Both image processing and machine learning algorithms are continuously refined to improve on this task. But little systematic comparison of such systems exist. What is the status quo? Do today's algorithms reach human performance? For assessing the performance of state-of-the-art machine learning algorithms, we present a publicly available traffic sign dataset with more than 50,000 images of German road signs in 43 classes. The data was considered in the second stage of the German Traffic Sign Recognition Benchmark held at IJCNN 2011. The results of this competition are reported and the best-performing algorithms are briefly described. Convolutional neural networks (CNNs) showed particularly high classification accuracies in the competition. We measured the performance of human subjects on the same data-and the CNNs outperformed the human test persons.