58,458 research outputs found
Data-Free Sketch-Based Image Retrieval
Rising concerns about privacy and anonymity preservation of deep learning
models have facilitated research in data-free learning (DFL). For the first
time, we identify that for data-scarce tasks like Sketch-Based Image Retrieval
(SBIR), where the difficulty in acquiring paired photos and hand-drawn sketches
limits data-dependent cross-modal learning algorithms, DFL can prove to be a
much more practical paradigm. We thus propose Data-Free (DF)-SBIR, where,
unlike existing DFL problems, pre-trained, single-modality classification
models have to be leveraged to learn a cross-modal metric-space for retrieval
without access to any training data. The widespread availability of pre-trained
classification models, along with the difficulty in acquiring paired
photo-sketch datasets for SBIR justify the practicality of this setting. We
present a methodology for DF-SBIR, which can leverage knowledge from models
independently trained to perform classification on photos and sketches. We
evaluate our model on the Sketchy, TU-Berlin, and QuickDraw benchmarks,
designing a variety of baselines based on state-of-the-art DFL literature, and
observe that our method surpasses all of them by significant margins. Our
method also achieves mAPs competitive with data-dependent approaches, all the
while requiring no training data. Implementation is available at
\url{https://github.com/abhrac/data-free-sbir}.Comment: Computer Vision and Pattern Recognition (CVPR) 202
An Empirical Comparison of Different Machine
Sketching has been used by humans to visualize and narrate the aesthetics of the world for a long time. With the onset of touch devices and augmented technologies, it has attracted more and more attention in recent years. Recognition of free-hand sketches is an extremely cumbersome and challenging task due to its abstract qualities and lack of visual cues. Most of the previous work has been done to identify objects in real pictorial images using neural networks instead of a more abstract depiction of the same objects in sketch. This research aims at comparing the performance of different machine learning algorithms and their learned inner representations. This research studies some of the famous machine learning models in classifying sketch images. It also does a study of legacy and the new datasets to classify a new sketch through various classifiers like support vector machines and the use of deep neural networks. It achieved remarkable results but still lacking behind the accuracy in the classification of the sketch images
Deep Embeddings for Robust User-Based Amateur Vocal Percussion Classification
Vocal Percussion Transcription (VPT) is concerned with the automatic detection and classification of vocal percussion sound events, allowing music creators and producers to sketch drum lines on the fly. Classifier algorithms in VPT systems learn best from small user-specific datasets, which usually restrict modelling to small input feature sets to avoid data overfitting. This study explores several deep supervised learning strategies to obtain informative feature sets for amateur vocal percussion classification. We evaluated the performance of these sets on regular vocal percussion classification tasks and compared them with several baseline approaches including feature selection methods and a speech recognition engine. These proposed learning models were supervised with several label sets containing information from four different levels of abstraction: instrument-level, syllable-level, phoneme-level, and boxeme-level. Results suggest that convolutional neural networks supervised with syllable-level annotations produced the most informative embeddings for classification, which can be used as input representations to fit classifiers with. Finally, we used back-propagation-based saliency maps to investigate the importance of different spectrogram regions for feature learning
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatically obtained by a structure-from-motion pipeline. The learned
representation is evaluated on a range of different tasks, providing
improvements on challenging cases of domain generalization, generic
sketch-based image retrieval or its fine-grained counterpart. In contrast to
other methods that learn a different model per task, object category, or
domain, we use the same network throughout all our experiments, achieving
state-of-the-art results in multiple benchmarks.Comment: ECCV 201
Multi-view Convolutional Neural Networks for 3D Shape Recognition
A longstanding question in computer vision concerns the representation of 3D
shapes for recognition: should 3D shapes be represented with descriptors
operating on their native 3D formats, such as voxel grid or polygon mesh, or
can they be effectively represented with view-based descriptors? We address
this question in the context of learning to recognize 3D shapes from a
collection of their rendered views on 2D images. We first present a standard
CNN architecture trained to recognize the shapes' rendered views independently
of each other, and show that a 3D shape can be recognized even from a single
view at an accuracy far higher than using state-of-the-art 3D shape
descriptors. Recognition rates further increase when multiple views of the
shapes are provided. In addition, we present a novel CNN architecture that
combines information from multiple views of a 3D shape into a single and
compact shape descriptor offering even better recognition performance. The same
architecture can be applied to accurately recognize human hand-drawn sketches
of shapes. We conclude that a collection of 2D views can be highly informative
for 3D shape recognition and is amenable to emerging CNN architectures and
their derivatives.Comment: v1: Initial version. v2: An updated ModelNet40 training/test split is
used; results with low-rank Mahalanobis metric learning are added. v3 (ICCV
2015): A second camera setup without the upright orientation assumption is
added; some accuracy and mAP numbers are changed slightly because a small
issue in mesh rendering related to specularities is fixe
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