Structured Learning of Tree Potentials in CRF for Image Segmentation

We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some pre-defined parametric models, and then methods like structured support vector machines (SSVMs) are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests---ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. Due to the rich structure and flexibility of decision trees, our approach is powerful in modelling complex data likelihoods and label relationships. The resulting optimization problem is very challenging because it can have exponentially many variables and constraints. We show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary (Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC 2012) segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and Learning System

Few-Shot Image Recognition by Predicting Parameters from Activations

In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods

Current stage of the ATCA follow-up for SPLASH

Four ground-state OH transitions were detected in emission, absorption and maser emission in the Southern Parkes Large-Area Survey in Hydroxyl (SPLASH). We re-observed these OH masers with the Australia Telescope Compact Array to obtain positions with high accuracy (~1 arcsec). According to the positions, we categorised these OH masers into different classes, i.e. star formation, evolved stars, supernova remnants and unknown origin. We found one interesting OH maser source (G336.644-0.695) in the pilot region, which has been studied in detail in Qiao et al. (2016a). In this paper, we present the current stage of the ATCA follow-up for SPLASH and discuss the potential future researches derived from the ATCA data.Comment: 2 pages, conference, IAU symposium 33

The catalogues and mid-infrared environment of Interstellar OH Masers

Data for a number of OH maser lines have been collected from surveys. The posi- tions are compared to recent mid-infrared (MIR) surveys such as Spitzer-GLIMPSE and WISE, restricting the comparison to point sources. The colors and intensities of the IR sources are compared. There are many 18 cm OH masers, but far fewer in lines arising from higher energy levels. We also make a comparison with the 5 cm Class II methanol masers. We have divided the results into 3 subsamples: those associated with OH masers only, those associated with OH masers and Class II methanol masers, and those only associated with Class II methanol masers. There are no obvious dif- ferences in the color-color or color-magnitude results for the GLIMPSE point sources. However, according to the results from the WISE 22 {\mu}m survey, the sources associ- ated with OH masers are brighter than those associated with methanol masers. We interpret the presence of OH and methanol masers mark the locations of regions where stars are forming. The OH masers are located on the borders of sharp features found in the IR. These are referred to as bubbles. If the OH masers mark the positions of protostars, the result provides indirect evidence for triggered star formation caused by the expansion of the bubbles.Comment: 23 pages (11 pages online only), 12 figures, Accepted. Monthly Notices of the Royal Astronomical Society,201