14,907 research outputs found
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.Comment: Accepted by ICCV 201
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD), which is the problem of learning
detectors using only image-level labels, has been attracting more and more
interest. However, this problem is quite challenging due to the lack of
location supervision. To address this issue, this paper integrates saliency
into a deep architecture, in which the location in- formation is explored both
explicitly and implicitly. Specifically, we select highly confident object pro-
posals under the guidance of class-specific saliency maps. The location
information, together with semantic and saliency information, of the selected
proposals are then used to explicitly supervise the network by imposing two
additional losses. Meanwhile, a saliency prediction sub-network is built in the
architecture. The prediction results are used to implicitly guide the
localization procedure. The entire network is trained end-to-end. Experiments
on PASCAL VOC demonstrate that our approach outperforms all state-of-the-arts.Comment: Accepted to appear in IJCAI 201
Deep Residual Learning for Image Recognition
Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced functions. We provide comprehensive empirical evidence
showing that these residual networks are easier to optimize, and can gain
accuracy from considerably increased depth. On the ImageNet dataset we evaluate
residual nets with a depth of up to 152 layers---8x deeper than VGG nets but
still having lower complexity. An ensemble of these residual nets achieves
3.57% error on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100
and 1000 layers.
The depth of representations is of central importance for many visual
recognition tasks. Solely due to our extremely deep representations, we obtain
a 28% relative improvement on the COCO object detection dataset. Deep residual
nets are foundations of our submissions to ILSVRC & COCO 2015 competitions,
where we also won the 1st places on the tasks of ImageNet detection, ImageNet
localization, COCO detection, and COCO segmentation.Comment: Tech repor
Speed/accuracy trade-offs for modern convolutional object detectors
The goal of this paper is to serve as a guide for selecting a detection
architecture that achieves the right speed/memory/accuracy balance for a given
application and platform. To this end, we investigate various ways to trade
accuracy for speed and memory usage in modern convolutional object detection
systems. A number of successful systems have been proposed in recent years, but
apples-to-apples comparisons are difficult due to different base feature
extractors (e.g., VGG, Residual Networks), different default image resolutions,
as well as different hardware and software platforms. We present a unified
implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016]
and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and
trace out the speed/accuracy trade-off curve created by using alternative
feature extractors and varying other critical parameters such as image size
within each of these meta-architectures. On one extreme end of this spectrum
where speed and memory are critical, we present a detector that achieves real
time speeds and can be deployed on a mobile device. On the opposite end in
which accuracy is critical, we present a detector that achieves
state-of-the-art performance measured on the COCO detection task.Comment: Accepted to CVPR 201
Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm
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