SRI Blog

-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 much mo

-By Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Jacqueline Aslarus, Wojciech Matusik Paper Link Todays paper is very interesting that sheds light on Machine Knitting. Converting Real and Synthetic inputs to machine learning pipeline to get instruction maps. Abstract Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting. We propose to tackle this problem by directly learning to synthesize regular machine instructions from real images. We create a cured dataset of real samples with their instruction counterpart and propose to use synthetic images to augment it in a novel way. We theoretically motivate our data mixing framework and show empirical results suggesting that making real images look more synthetic is beneficial in our problem setup.

By - Swetha Sirnam, Anand Mishra,Guru Prasad Hegde, C V Jawahar Post Link Abstract  Accurately annotated large video data is critical for the development of reliable surveillance and automotive related vision solutions. Paper proposes an efficient and yet accurate annotation scheme for objects in videos (pedestrians in this case) with minimal supervision. They annotate objects with tight bounding boxes. They propagate the annotations across the frames with a self training based approach. An energy minimization scheme for the segmentation is the central component of our method. Unlike the popular grab cut like segmentation schemes, we demand minimal user intervention. Since our annotation is built on an accurate segmentation, our bounding boxes are tight. Approach is  validated the performance on multiple publicly available datasets.  Paper focus on efficient object bounding box annotation in videos and accurate human pose annotation in videos. Object Annotation in Vi