57 research outputs found
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy
Besides the complex nature of colonoscopy frames with intrinsic frame
formation artefacts such as light reflections and the diversity of polyp
types/shapes, the publicly available polyp segmentation training datasets are
limited, small and imbalanced. In this case, the automated polyp segmentation
using a deep neural network remains an open challenge due to the overfitting of
training on small datasets. We proposed a simple yet effective polyp
segmentation pipeline that couples the segmentation (FCN) and classification
(CNN) tasks. We find the effectiveness of interactive weight transfer between
dense and coarse vision tasks that mitigates the overfitting in learning. And
It motivates us to design a new training scheme within our segmentation
pipeline. Our method is evaluated on CVC-EndoSceneStill and Kvasir-SEG
datasets. It achieves 4.34% and 5.70% Polyp-IoU improvements compared to the
state-of-the-art methods on the EndoSceneStill and Kvasir-SEG datasets,
respectively.Comment: 11 pages, 10 figures, submit versio
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
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