481 research outputs found
Multi Branch Siamese Network For Person Re-Identification
To capture robust person features, learning discriminative,
style and view invariant descriptors is a key challenge in person Re-Identification (re-id). Most deep Re-ID models learn
single scale feature representation which are unable to grasp
compact and style invariant representations. In this paper,
we present a multi branch Siamese Deep Neural Network
with multiple classifiers to overcome the above issues. The
multi-branch learning of the network creates a stronger descriptor with fine-grained information from global features of
a person. Camera to camera image translation is performed
with generative adversarial network to generate diverse data
and add style invariance in learned features. Experimental
results on benchmark datasets demonstrate that the proposed
method performs better than other state of the arts methods
Self Attention based multi branch Network for Person Re-Identification
2noRecent progress in the field of person re-identification have shown promising improvement by designing neural networks to learn most discriminative features representations. Some efforts utilize similar parts from different locations to learn better representation with the help of soft attention, while others search for part based learning methods to enhance consecutive regions relationships in the learned features. However, only few attempts have been made to learn non-local similar parts directly for the person re-identification problem. In this paper, we propose a novel self attention based multi branch(classifier) network to directly model long-range dependencies in the learned features. Multi classifiers assist the model to learn discriminative features while self attention module encourages the learning to be independent of the feature map locations. Spectral normalization is applied in the whole network to improve the training dynamics and for the better convergence of the model. Experimental results on two benchmark datasets have shown the robustness of the proposed work.openopenMunir A.; Micheloni C.Munir, A.; Micheloni, C
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details
This paper presents MRPyrNet, a new convolutional neural network (CNN) architecture that improves the capabilities of CNN-based pipelines for knee injury detection via magnetic resonance imaging (MRI). Existing works showed that anomalies are localized in small-sized knee regions that appear in particular areas of MRI scans. Based on such facts, MRPyrNet exploits a Feature Pyramid Network to enhance small appearing features and Pyramidal Detail Pooling to capture such relevant information in a robust way. Experimental results on two publicly available datasets demonstrate that MRPyrNet improves the ACL tear and meniscal tear diagnosis capabilities of two state-of-the-art methodologies. Code is available at https://git.io/JtMPH
An Attention-driven Hierarchical Multi-scale Representation for Visual Recognition
Convolutional Neural Networks (CNNs) have revolutionized the understanding of
visual content. This is mainly due to their ability to break down an image into
smaller pieces, extract multi-scale localized features and compose them to
construct highly expressive representations for decision making. However, the
convolution operation is unable to capture long-range dependencies such as
arbitrary relations between pixels since it operates on a fixed-size window.
Therefore, it may not be suitable for discriminating subtle changes (e.g.
fine-grained visual recognition). To this end, our proposed method captures the
high-level long-range dependencies by exploring Graph Convolutional Networks
(GCNs), which aggregate information by establishing relationships among
multi-scale hierarchical regions. These regions consist of smaller (closer
look) to larger (far look), and the dependency between regions is modeled by an
innovative attention-driven message propagation, guided by the graph structure
to emphasize the neighborhoods of a given region. Our approach is simple yet
extremely effective in solving both the fine-grained and generic visual
classification problems. It outperforms the state-of-the-arts with a
significant margin on three and is very competitive on other two datasets.Comment: Accepted in the 32nd British Machine Vision Conference (BMVC) 202
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