695 research outputs found
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general
setting where only a single unlabeled image is given. This is far more
challenge than typical co-localization or weakly-supervised localization tasks.
To tackle this problem, we propose a simple but effective pattern mining-based
method, called Object Location Mining (OLM), which exploits the advantages of
data mining and feature representation of pre-trained convolutional neural
networks (CNNs). Specifically, we first convert the feature maps from a
pre-trained CNN model into a set of transactions, and then discovers frequent
patterns from transaction database through pattern mining techniques. We
observe that those discovered patterns, i.e., co-occurrence highlighted
regions, typically hold appearance and spatial consistency. Motivated by this
observation, we can easily discover and localize possible objects by merging
relevant meaningful patterns. Extensive experiments on a variety of benchmarks
demonstrate that OLM achieves competitive localization performance compared
with the state-of-the-art methods. We also evaluate our approach compared with
unsupervised saliency detection methods and achieves competitive results on
seven benchmark datasets. Moreover, we conduct experiments on fine-grained
classification to show that our proposed method can locate the entire object
and parts accurately, which can benefit to improving the classification results
significantly
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing
approaches are prone to trapping in the discriminative object parts, e.g.,
finding the face of a cat instead of the full body, due to lacking the
supervision on the extent of full objects. To address this challenge, we
incorporate object segmentation into the detector training, which guides the
model to correctly localize the full objects. We propose the multiple instance
curriculum learning (MICL) method, which injects curriculum learning (CL) into
the multiple instance learning (MIL) framework. The MICL method starts by
automatically picking the easy training examples, where the extent of the
segmentation masks agree with detection bounding boxes. The training set is
gradually expanded to include harder examples to train strong detectors that
handle complex images. The proposed MICL method with segmentation in the loop
outperforms the state-of-the-art weakly supervised object detectors by a
substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for
unconstrained salient object detection in images using deep convolutional
neural networks. Instead of generating thousands of candidate bounding boxes
and refining them, our network directly learns to generate the saliency map
containing the exact number of salient objects. During training, we convert the
ground-truth rectangular boxes to Gaussian distributions that better capture
the ROI regarding individual salient objects. During inference, the network
predicts Gaussian distributions centered at salient objects with an appropriate
covariance, from which bounding boxes are easily inferred. Notably, our network
performs saliency map prediction without pixel-level annotations, salient
object detection without object proposals, and salient object subitizing
simultaneously, all in a single pass within a unified framework. Extensive
experiments show that our approach outperforms existing methods on various
datasets by a large margin, and achieves more than 100 fps with VGG16 network
on a single GPU during inference
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