1,889 research outputs found
Object Detection based on Region Decomposition and Assembly
Region-based object detection infers object regions for one or more
categories in an image. Due to the recent advances in deep learning and region
proposal methods, object detectors based on convolutional neural networks
(CNNs) have been flourishing and provided the promising detection results.
However, the detection accuracy is degraded often because of the low
discriminability of object CNN features caused by occlusions and inaccurate
region proposals. In this paper, we therefore propose a region decomposition
and assembly detector (R-DAD) for more accurate object detection.
In the proposed R-DAD, we first decompose an object region into multiple
small regions. To capture an entire appearance and part details of the object
jointly, we extract CNN features within the whole object region and decomposed
regions. We then learn the semantic relations between the object and its parts
by combining the multi-region features stage by stage with region assembly
blocks, and use the combined and high-level semantic features for the object
classification and localization. In addition, for more accurate region
proposals, we propose a multi-scale proposal layer that can generate object
proposals of various scales. We integrate the R-DAD into several feature
extractors, and prove the distinct performance improvement on PASCAL07/12 and
MSCOCO18 compared to the recent convolutional detectors.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligence (AAAI
Towards Robust Curve Text Detection with Conditional Spatial Expansion
It is challenging to detect curve texts due to their irregular shapes and
varying sizes. In this paper, we first investigate the deficiency of the
existing curve detection methods and then propose a novel Conditional Spatial
Expansion (CSE) mechanism to improve the performance of curve text detection.
Instead of regarding the curve text detection as a polygon regression or a
segmentation problem, we treat it as a region expansion process. Our CSE starts
with a seed arbitrarily initialized within a text region and progressively
merges neighborhood regions based on the extracted local features by a CNN and
contextual information of merged regions. The CSE is highly parameterized and
can be seamlessly integrated into existing object detection frameworks.
Enhanced by the data-dependent CSE mechanism, our curve text detection system
provides robust instance-level text region extraction with minimal
post-processing. The analysis experiment shows that our CSE can handle texts
with various shapes, sizes, and orientations, and can effectively suppress the
false-positives coming from text-like textures or unexpected texts included in
the same RoI. Compared with the existing curve text detection algorithms, our
method is more robust and enjoys a simpler processing flow. It also creates a
new state-of-art performance on curve text benchmarks with F-score of up to
78.4.Comment: This paper has been accepted by IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR 2019
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