10,179 research outputs found
LSTD: A Low-Shot Transfer Detector for Object Detection
Recent advances in object detection are mainly driven by deep learning with
large-scale detection benchmarks. However, the fully-annotated training set is
often limited for a target detection task, which may deteriorate the
performance of deep detectors. To address this challenge, we propose a novel
low-shot transfer detector (LSTD) in this paper, where we leverage rich
source-domain knowledge to construct an effective target-domain detector with
very few training examples. The main contributions are described as follows.
First, we design a flexible deep architecture of LSTD to alleviate transfer
difficulties in low-shot detection. This architecture can integrate the
advantages of both SSD and Faster RCNN in a unified deep framework. Second, we
introduce a novel regularized transfer learning framework for low-shot
detection, where the transfer knowledge (TK) and background depression (BD)
regularizations are proposed to leverage object knowledge respectively from
source and target domains, in order to further enhance fine-tuning with a few
target images. Finally, we examine our LSTD on a number of challenging low-shot
detection experiments, where LSTD outperforms other state-of-the-art
approaches. The results demonstrate that LSTD is a preferable deep detector for
low-shot scenarios.Comment: Accepted by AAAI201
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to guide
the search for objects, thereby avoiding exhaustive sliding window search
across images. Despite the popularity and widespread use of detection
proposals, it is unclear which trade-offs are made when using them during
object detection. We provide an in-depth analysis of twelve proposal methods
along with four baselines regarding proposal repeatability, ground truth
annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM,
R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object
detection improving proposal localisation accuracy is as important as improving
recall. We introduce a novel metric, the average recall (AR), which rewards
both high recall and good localisation and correlates surprisingly well with
detection performance. Our findings show common strengths and weaknesses of
existing methods, and provide insights and metrics for selecting and tuning
proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment
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