MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (i.e. atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.Comment: 10 pages including referenc

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.Comment: 14 pages, 12 figure

Miami-Dade County Urban Tree Canopy Assessment

This assessment focuses on the environmental and socioeconomic impacts from the urban tree canopy (UTC) within the Urban Development Boundary of Miami-Dade County, as defined by the Miami-Dade County MPO (Figure 1). The area (intracoastal water areas excluded) encompasses approximately 1150 km 2 (444 mi 2). A combination of remote sensing and publicly available vector data was used to classify the following land cover classes: tree canopy/shrubs, grass, bare ground, wetland, water, building, street/railroad, other impervious surfaces, and cropland

The iNaturalist Species Classification and Detection Dataset

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.Comment: CVPR 201

Miami- Dade Urban Tree Canopy Analysis

Two of the Florida state universities, University of Florida (UF) and Florida International University (FIU), collaborated in assessing urban tree cover (UTC) for part of northwestern Miami-Dade County, covering an area of approximately 380 km2 (147 mi2). The analysis estimated the area with current tree canopy (existing UTC), the area of potential tree canopy (possible UTC), and various other land cover categories. The assessment used two methods to establish those estimates. The first method utilized the i-Tree canopy assessment tool provided by the USDA Forest Service. The second method used a combination of multispectral satellite data and airborne Light Detection and Ranging (LiDAR) datasets for detection and classification of land cover. Classification results were further analyzed in a Geographic Information System (GIS) to relate land cover distribution patterns (obtained from the second land cover classification method) to surface temperatures, land use patterns, and socioeconomic factors