56 research outputs found
Click Carving: Segmenting Objects in Video with Point Clicks
We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking the lead even in initialization. In particular, for a given video frame,
the system precomputes a ranked list of thousands of possible segmentation
hypotheses (also referred to as object region proposals) using image and motion
cues. Then, the user looks at the top ranked proposals, and clicks on the
object boundary to carve away erroneous ones. This process iterates (typically
2-3 times), and each time the system revises the top ranked proposal set, until
the user is satisfied with a resulting segmentation mask. Finally, the mask is
propagated across the video to produce a spatio-temporal object tube. On three
challenging datasets, we provide extensive comparisons with both existing work
and simpler alternative methods. In all, the proposed Click Carving approach
strikes an excellent balance of accuracy and human effort. It outperforms all
similarly fast methods, and is competitive or better than those requiring 2 to
12 times the effort.Comment: A preliminary version of the material in this document was filed as
University of Texas technical report no. UT AI16-0
Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss
Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants. Crowd counting is one of the keys to automatic crowd behaviour analysis. Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in recent years. Researchers have devoted much effort to the design of variant CNN architectures and most of them are based on the pre-trained VGG16 model. Due to the insufficient expressive capacity, the backbone network of VGG16 is usually followed by another cumbersome network specially designed for good counting performance. Although VGG models have been outperformed by Inception models in image classification tasks, the existing crowd counting networks built with Inception modules still only have a small number of layers with basic types of Inception modules. To fill in this gap, in this paper, we firstly benchmark the baseline Inception-v3 model on commonly used crowd counting datasets and achieve surprisingly good performance comparable with or better than most existing crowd counting models. Subsequently, we push the boundary of this disruptive work further by proposing a Segmentation Guided Attention Network (SGANet) with Inception-v3 as the backbone and a novel curriculum loss for crowd counting. We conduct thorough experiments to compare the performance of our SGANet with prior arts and the proposed model can achieve state-of-the-art performance with MAE of 57.6, 6.3 and 87.6 on ShanghaiTechA, ShanghaiTechB and UCF_QNRF, respectivel
A comprehensive survey on deep active learning and its applications in medical image analysis
Deep learning has achieved widespread success in medical image analysis,
leading to an increasing demand for large-scale expert-annotated medical image
datasets. Yet, the high cost of annotating medical images severely hampers the
development of deep learning in this field. To reduce annotation costs, active
learning aims to select the most informative samples for annotation and train
high-performance models with as few labeled samples as possible. In this
survey, we review the core methods of active learning, including the evaluation
of informativeness and sampling strategy. For the first time, we provide a
detailed summary of the integration of active learning with other
label-efficient techniques, such as semi-supervised, self-supervised learning,
and so on. Additionally, we also highlight active learning works that are
specifically tailored to medical image analysis. In the end, we offer our
perspectives on the future trends and challenges of active learning and its
applications in medical image analysis.Comment: Paper List on Github:
https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysi
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