FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation

Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have covered a wide range of object categories, there are still a significant number of objects that are not included. Can we perform the same task without a lot of human annotations? In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only. To evaluate and validate the performance of our approach, we have built a few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our dataset contains significant number of objects that have never been seen or annotated in previous datasets, such as tiny daily objects, merchandise, cartoon characters, logos, etc. We build our baseline model using standard backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise, we found that training our model from scratch using FSS-1000 achieves comparable and even better results than training with weights pre-trained by ImageNet which is more than 100 times larger than FSS-1000. Both our approach and dataset are simple, effective, and easily extensible to learn segmentation of new object classes given very few annotated training examples. Dataset is available at https://github.com/HKUSTCV/FSS-1000

Tom McCall Award Reception Program

Program for a reception held at the Governor\u27s Ceremonial Office honoring Bob Straub for winning 1000 Friends of Oregon\u27s 1999 Tom McCall Award, recognizing his commitment to environmental conservation. The reverse side of the program includes a copy of the Board of Directors\u27 resolution honoring Bob Straub

Top 10 Photobooks of 2018

An annual tribute to some of the exceptional photobook releases from 2018 – selected by 1000 Words Editor in Chief, Tim Clark

Genesis of the 1000-Foot Arecibo Dish

The giant radar/radio astronomy dish near Arecibo, Puerto Rico, was conceived by William E. Gordon in early 1958 as a back-scattering radar system to measure the density and temperature of the Earth’s ionosphere up to a few thousand kilometers. Gordon calculated the required size of the antenna by using the Thomson cross-section for scattering by the electrons, and assuming that the elementary scattered waves would be incoherent. During the summer and autumn of 1958 Gordon led a study group that published a design report in December 1958. The report showed that a dish 1000 feet in diameter would be required, and described a limestone sinkhole in Puerto Rico that would make a suitable support for such a dish. Meanwhile, in November 1958, Kenneth L. Bowles per-formed an ionospheric radar experiment that showed that the Gordon calculation for the scattered power was roughly correct, but that the calculated spectral width was too big. The consequence of these results was that a dish substantially smaller than 1000 feet could have satisfied the original goals for the radar. However, from the spring of 1958 the value of 1000 feet had been in the minds of the study team, and a large suite of important experiments that such a dish could do had been identified. These apparently became the raison d’être for the project, and the possibility of shrinking the dish to accomplish only the original goals seems to have been ignored. The project was sold to a new federal funding agency, the Advanced Research Projects Agency (ARPA), which was interested, in part at least, because ballistic missiles traveled through the ionosphere and it was important to fully understand that environment. Gordon’s original calculation contained a remarkably beneficial error. Without it, it is doubtful that such a large dish would have been built

Special Libraries, January 1938

Volume 29, Issue 1https://scholarworks.sjsu.edu/sla_sl_1938/1000/thumbnail.jp