Iterative Object and Part Transfer for Fine-Grained Recognition

The aim of fine-grained recognition is to identify sub-ordinate categories in images like different species of birds. Existing works have confirmed that, in order to capture the subtle differences across the categories, automatic localization of objects and parts is critical. Most approaches for object and part localization relied on the bottom-up pipeline, where thousands of region proposals are generated and then filtered by pre-trained object/part models. This is computationally expensive and not scalable once the number of objects/parts becomes large. In this paper, we propose a nonparametric data-driven method for object and part localization. Given an unlabeled test image, our approach transfers annotations from a few similar images retrieved in the training set. In particular, we propose an iterative transfer strategy that gradually refine the predicted bounding boxes. Based on the located objects and parts, deep convolutional features are extracted for recognition. We evaluate our approach on the widely-used CUB200-2011 dataset and a new and large dataset called Birdsnap. On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.Comment: To appear in ICME 2017 as an oral pape

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

Towards Instance-level Image-to-Image Translation

Unpaired Image-to-image Translation is a new rising and challenging vision problem that aims to learn a mapping between unaligned image pairs in diverse domains. Recent advances in this field like MUNIT and DRIT mainly focus on disentangling content and style/attribute from a given image first, then directly adopting the global style to guide the model to synthesize new domain images. However, this kind of approaches severely incurs contradiction if the target domain images are content-rich with multiple discrepant objects. In this paper, we present a simple yet effective instance-aware image-to-image translation approach (INIT), which employs the fine-grained local (instance) and global styles to the target image spatially. The proposed INIT exhibits three import advantages: (1) the instance-level objective loss can help learn a more accurate reconstruction and incorporate diverse attributes of objects; (2) the styles used for target domain of local/global areas are from corresponding spatial regions in source domain, which intuitively is a more reasonable mapping; (3) the joint training process can benefit both fine and coarse granularity and incorporates instance information to improve the quality of global translation. We also collect a large-scale benchmark for the new instance-level translation task. We observe that our synthetic images can even benefit real-world vision tasks like generic object detection.Comment: Accepted to CVPR 2019. Project page: http://zhiqiangshen.com/projects/INIT/index.htm

A ferrofluid-based homogeneous assay for highly sensitive and selective detection of single-nucleotide polymorphisms

10.1039/C3CC43281EChemical Communications49738114-811