Salient Object Detection via Augmented Hypotheses

In this paper, we propose using \textit{augmented hypotheses} which consider objectness, foreground and compactness for salient object detection. Our algorithm consists of four basic steps. First, our method generates the objectness map via objectness hypotheses. Based on the objectness map, we estimate the foreground margin and compute the corresponding foreground map which prefers the foreground objects. From the objectness map and the foreground map, the compactness map is formed to favor the compact objects. We then derive a saliency measure that produces a pixel-accurate saliency map which uniformly covers the objects of interest and consistently separates fore- and background. We finally evaluate the proposed framework on two challenging datasets, MSRA-1000 and iCoSeg. Our extensive experimental results show that our method outperforms state-of-the-art approaches.Comment: IJCAI 2015 pape

Sense Beyond Expressions: Cuteness

With the development of Internet culture, cuteness has become a popular concept. Many people are curious about what factors making a person look cute. However, there is rare research to answer this interesting question. In this work, we construct a dataset of personal images with comprehensively annotated cuteness scores and facial attributes to investigate this high-level concept in depth. Based on this dataset, through an automatic attributes mining process, we find several critical attributes determining the cuteness of a person. We also develop a novel Continuous Latent Support Vector Machine (C-LSVM) method to predict the cuteness score of one person given only his image. Extensive evaluations validate the effectiveness of the proposed method for cuteness prediction.Comment: 4 page

Adaptive Nonparametric Image Parsing

In this paper, we present an adaptive nonparametric solution to the image parsing task, namely annotating each image pixel with its corresponding category label. For a given test image, first, a locality-aware retrieval set is extracted from the training data based on super-pixel matching similarities, which are augmented with feature extraction for better differentiation of local super-pixels. Then, the category of each super-pixel is initialized by the majority vote of the $k$-nearest-neighbor super-pixels in the retrieval set. Instead of fixing $k$ as in traditional non-parametric approaches, here we propose a novel adaptive nonparametric approach which determines the sample-specific k for each test image. In particular, $k$ is adaptively set to be the number of the fewest nearest super-pixels which the images in the retrieval set can use to get the best category prediction. Finally, the initial super-pixel labels are further refined by contextual smoothing. Extensive experiments on challenging datasets demonstrate the superiority of the new solution over other state-of-the-art nonparametric solutions.Comment: 11 page

GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of re-usable and multi-purpose knowledge, and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in

Few-Shot Object Detection via Synthetic Features with Optimal Transport

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address that limitation, in this paper, we propose a novel approach in which we train a generator to generate synthetic data for novel classes. Still, directly training a generator on the novel class is not effective due to the lack of novel data. To overcome that issue, we leverage the large-scale dataset of base classes. Our overarching goal is to train a generator that captures the data variations of the base dataset. We then transform the captured variations into novel classes by generating synthetic data with the trained generator. To encourage the generator to capture data variations on base classes, we propose to train the generator with an optimal transport loss that minimizes the optimal transport distance between the distributions of real and synthetic data. Extensive experiments on two benchmark datasets demonstrate that the proposed method outperforms the state of the art. Source code will be available

ANALYZE THE TREATMENT REGIMENS AND THROMBOSIS PROPHYLAXIS USED IN CORONARY ARTERY INTERVENTION AT INTERVENTIONAL CARDIOLOGY UNIT IN CAN THO CENTRAL GENERAL HOSPITAL

Objective: The study was conducted to analyze the rationality of treatment regimens and thrombosis prophylaxis used in coronary artery intervention to compare to guidelines for treatment according to VNHA and recommendation of ACC/AHA at Interventional cardiology in Can Tho Central General Hospital. Methods: The cross-sectional study was based on the data collected from entire medical records of patients at Interventional cardiology in Can Tho Central General Hospital from August 2017 to February 2018. The rationality of the antithrombotic regimen used at the Hospital is assessed through criteria such as medical combination, dosage, time to take medicine, clinical trials during the treatment. Results: The study found that 95.6% and 90.7% were suitable for medical combination before and after PCI; 100% fit for the use of medicine; and 100% was suitable for antithrombotic agents and clinical trials during treatment time; in terms of dosage, the result showed that entrance and maintenance were 84.9% and 100% for aspirin respectively; 71.7% and 100% for clopidogrel; 100% and 94.7% for ticagrelor; 90.2-92.8% and 98.1% for enoxaparin; especially, heparin-100% anticoagulant was appropriate to recommend. Conclusion: The study showed that treatment regimens and thrombosis prophylaxis in percutaneous coronary intervention at Interventional cardiology in Can Tho Central General Hospital were quite suitable compared to the recommendations of the Heart Association. The results from the study are a scientific basis for the Hospital to maintain or consider adjustments to improve the quality of treatment, ensure the effectiveness and safety of patients

Comparing Traditional Body Mass Index and Joslin Diabetes Center’s Asian Body Mass Index in Predicting Self-Report Type 2 Diabetes.

This study examined the predictability of traditional Body Mass Index standards and the Joslin Diabetes Center’s recommended BMI standards for Asian Americans. A sample of 2973 adult Asian Americans aged 45 and older from the 2009 California Health Interview Survey (CHIS) was used. This sample consists of 12.25% of respondents with type 2 diabetes and 87.75% that had neither type 2 or any types of diabetes. Logistic regression was used to estimate the predictability of two the BMI standards and to test for the interaction effect of BMI standards and sex in predicting type 2 diabetes. The results revealed that both traditional and Joslin Diabetes Center’s recommended standards had similar predictability of types 2 diabetes. Both BMI standards of overweight and obesity had a greater association with type 2 diabetes for men than for women. That is, given a similar level of BMI, men tend to report a greater prevalence of type 2 diabetes than women. These findings support caution in changing BMI cut-offs for Asian Americans, and highlight the potential limitations of using BMI as a measure of risk for diabetes in this population