SRI Blog

-By Sheng-Yu Wang Oliver Wang Andrew Owens Richard Zhang Alexei A. Efros  UC Berkeley1 Adobe Research Paper Link   Git Code Abstract Most malicious photo manipulations are created using standard image editing tools, such as Adobe. We present a method for detecting one very popular Photoshop manipulation image warping applied to human faces – using a model trained entirely using fake images that were automatically generated by scripting Photoshop itself. We show that our model outperforms humans at the task of recognizing manipulated images, can predict the specific location of edits, and in some cases can be used to “undo” a manipulation to reconstruct the original, unedited image. We demonstrate that the system can be successfully applied to real, artist-created image manipulations In an era when digitally edited visual content is ubiquitous, the public is justifiably eager to know whether the images they see on TV, in glossy magazines and on the Inter...

-By Zhiheng Ma, Xing Wei Xiaopeng Hong, Yihong Gong Research Center for Artificial Intelligence, Peng Cheng Laboratory Paper Link Abstract In crowd counting datasets, each person is annotated by a point, which is usually the center of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of-the-art methods are based on density map estimation, which convert the sparse point annotations into a “ground truth” density map through a Gaussian kernel, and then use it as the learning target to train a density map estimator. However, such a “ground-truth” density map is imperfect due to occlusions, perspective effects, variations in object shapes, etc. On the contrary, we propose Bayesian loss, a novel loss function which constructs a density contribution probability model from the point annotations. Instead of constraining the value at every pixel in the density map, the proposed training loss adopts a more reliable supervision on the count ex...

-By Oscar Chang, Hod Lipson Paper Link This paper presents few common myths in machine learning. Tries to explain each of them. Myth 1: TensorFlow is a library for working with tensors. Myth 2: Image databases reflect real photographs found in nature. Myth 3: MO researchers do not use test kits for testing. Myth 4: All input data is used in neural network training. Myth 5: Learning for very deep residual networks requires packet normalization. Myth 6: Networks with attention [attention] are better than convolution [convolution]. Myth 7: Cards of Significance — A Reliable Way to Interpret Neural Networks. Myth 1: TensorFlow is a library for working with tensors In fact, this is a library for working with matrices, and this difference is quite significant. In Computing Higher Order Derivatives of Matrix and Tensor Expressions. Laue et al. NeurIPS 2018 authors demonstrate that their automatic differentiation library, based on real tensor calculus, has muc...