When Deep Learning Meets Digital Image Correlation

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

Conventionally, image denoising and high-level vision tasks are handled separately in computer vision, and their connection is fragile. In this paper, we cope with the two jointly and explore the mutual influence between them with the focus on two questions, namely (1) how image denoising can help solving high-level vision problems, and (2) how the semantic information from high-level vision ta...

When Semi-supervised Learning Meets Ensemble Learning

Semi-supervised learning and ensemble learning are two important machine learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. Although both paradigms have achieved great success during the past decade, they were almost developed separately. In this paper, we advocat...

When image indexing meets knowledge discovery

In our paper, we deal with the challenge of extending automatically the classic image indexing by visual relationship features. The visual relationship features are discovered automatically from images. They contribute to make more efficient the content-based indexing. More particularly, we develop an advanced content-based indexing articulated around the following notions : classic indexing, c...

Digital Image Correlation

TensorLog: Deep Learning Meets Probabilistic DBs

We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neuralnetwork infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning framework...