Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201

Homage to Professor Shinko Ogiwara

<p><b><i>Primula undulifolia</i> sp. nov.</b> (A) Habit in Flowering; (B) Type Locality; (C) Calyx; (D) Pin and Thrum Flowers; (E) Leaf. Photographed by Yuan XU.</p

Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two-branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle-level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora

Probabilistic Image Colorization

We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset

What are the possible futures impacts of patient opinion leaders on healthcare and healthcare stakeholders ?

The aim of this project is to find out what are the potential future impacts of Patient Opinion Leaders (POLS) on healthcare and healthcare stakeholders. Because there exists many different definitions for POLS, the following definition will be consistently used for the sake of this project: A Patient Opinion Leader is a patient that suffers (or has suffered) from (a) chronic disease(s), either mental or physical, and that shares his/her knowledge about his/her condition and treatment on the Internet through blogs, videos, social media or community websites. In order to conduct my project, I interviewed ten people with close ties to the healthcare industry. To conduct the interviews, I used the Futures Wheel method. The goal of this method is to draft a wheel that is used to identify secondary and tertiary consequences of a certain event (here: the future of POLS). Once all of the ten interviews had been conducted and the final Futures Wheel drafted, the data from the wheels was input into the Gephi computer software by Mr. Pierre- Alexandre Fonta, Big Data – engineer, Data Scientist and assistant at the University of Applied Sciences in Geneva. Gephi is an “interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs”1. It is used to develop cartographies in order to visualize a certain event or question. Once the final cartography was elaborated, I proceeded to discuss it with three of the ten individuals I had interviewed. Each of the three people interviewed came up with a realistic and feasible scenario for the future in regards to Patient Opinion Leaders

Solving Gauss' Laws and Searching Dirac Observables for the Four Interactions

A review is given of the status of the program of classical reduction to Dirac's observables of the four interactions (standard SU(3)xSU(2)xU(1) particle model and tetrad gravity) with the matter described either by Grassmann-valued fermion fields or by particles with Grassmann charges.Comment: 9 pages, LaTeX (using espcrc2.sty). Talk given at the Second Conf. on Constrained Dynamics and Quantum Gravity, S.Margherita Ligure, 17-21 September 199

Gauge Boson Self Couplings and four fermion final states at LEP

Four-fermion productions measured in the LEP2 data are reviewed. The total and differential cross-section yields represent the first clear evidence for the existence of gauge boson self couplings, in support of the non-abelian SU(2)xU(1) structure of the electroweak model, at the percent level.Comment: 4 pages, 3 figures. To appear in the Proceedings of 'XV IFAE - Incontri sulla Fisica delle Alte Energie', 23-26 April 2003, Lecce, Ital