3,875 research outputs found
LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
LiveSketch is a novel algorithm for searching large image collections using
hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch
search by creating visual suggestions that augment the query as it is drawn,
making query specification an iterative rather than one-shot process that helps
disambiguate users' search intent. Our technical contributions are: a triplet
convnet architecture that incorporates an RNN based variational autoencoder to
search for images using vector (stroke-based) queries; real-time clustering to
identify likely search intents (and so, targets within the search embedding);
and the use of backpropagation from those targets to perturb the input stroke
sequence, so suggesting alterations to the query in order to guide the search.
We show improvements in accuracy and time-to-task over contemporary baselines
using a 67M image corpus.Comment: Accepted to CVPR 201
Novel hybrid generative adversarial network for synthesizing image from sketch
In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image which is processed by generator module running 3 different deep learning modes of ResNet, MobileNet, and U-Net. The discriminator module processes the input of real images as well as output from generator. With a novel interactive communication between generator and discriminator, the proposed model offers optimal retrieval performance along with an inclusion of optimizer. The study outcome shows significant performance improvement
Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework
This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this recordACCV 2018:
14th Asian Conference on Computer Vision, Perth, Australia, 2-6 December 2018In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.European Union Horizon 2020CERCA Program of Generalitat de Cataluny
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions
In the rapidly advancing field of multi-modal machine learning (MMML), the
convergence of multiple data modalities has the potential to reshape various
applications. This paper presents a comprehensive overview of the current
state, advancements, and challenges of MMML within the sphere of engineering
design. The review begins with a deep dive into five fundamental concepts of
MMML:multi-modal information representation, fusion, alignment, translation,
and co-learning. Following this, we explore the cutting-edge applications of
MMML, placing a particular emphasis on tasks pertinent to engineering design,
such as cross-modal synthesis, multi-modal prediction, and cross-modal
information retrieval. Through this comprehensive overview, we highlight the
inherent challenges in adopting MMML in engineering design, and proffer
potential directions for future research. To spur on the continued evolution of
MMML in engineering design, we advocate for concentrated efforts to construct
extensive multi-modal design datasets, develop effective data-driven MMML
techniques tailored to design applications, and enhance the scalability and
interpretability of MMML models. MMML models, as the next generation of
intelligent design tools, hold a promising future to impact how products are
designed
A Recipe for Efficient SBIR Models: Combining Relative Triplet Loss with Batch Normalization and Knowledge Distillation
Sketch-Based Image Retrieval (SBIR) is a crucial task in multimedia
retrieval, where the goal is to retrieve a set of images that match a given
sketch query. Researchers have already proposed several well-performing
solutions for this task, but most focus on enhancing embedding through
different approaches such as triplet loss, quadruplet loss, adding data
augmentation, and using edge extraction. In this work, we tackle the problem
from various angles. We start by examining the training data quality and show
some of its limitations. Then, we introduce a Relative Triplet Loss (RTL), an
adapted triplet loss to overcome those limitations through loss weighting based
on anchors similarity. Through a series of experiments, we demonstrate that
replacing a triplet loss with RTL outperforms previous state-of-the-art without
the need for any data augmentation. In addition, we demonstrate why batch
normalization is more suited for SBIR embeddings than l2-normalization and show
that it improves significantly the performance of our models. We further
investigate the capacity of models required for the photo and sketch domains
and demonstrate that the photo encoder requires a higher capacity than the
sketch encoder, which validates the hypothesis formulated in [34]. Then, we
propose a straightforward approach to train small models, such as ShuffleNetv2
[22] efficiently with a marginal loss of accuracy through knowledge
distillation. The same approach used with larger models enabled us to
outperform previous state-of-the-art results and achieve a recall of 62.38% at
k = 1 on The Sketchy Database [30]
Towards Practicality of Sketch-Based Visual Understanding
Sketches have been used to conceptualise and depict visual objects from
pre-historic times. Sketch research has flourished in the past decade,
particularly with the proliferation of touchscreen devices. Much of the
utilisation of sketch has been anchored around the fact that it can be used to
delineate visual concepts universally irrespective of age, race, language, or
demography. The fine-grained interactive nature of sketches facilitates the
application of sketches to various visual understanding tasks, like image
retrieval, image-generation or editing, segmentation, 3D-shape modelling etc.
However, sketches are highly abstract and subjective based on the perception of
individuals. Although most agree that sketches provide fine-grained control to
the user to depict a visual object, many consider sketching a tedious process
due to their limited sketching skills compared to other query/support
modalities like text/tags. Furthermore, collecting fine-grained sketch-photo
association is a significant bottleneck to commercialising sketch applications.
Therefore, this thesis aims to progress sketch-based visual understanding
towards more practicality.Comment: PhD thesis successfully defended by Ayan Kumar Bhunia, Supervisor:
Prof. Yi-Zhe Song, Thesis Examiners: Prof Stella Yu and Prof Adrian Hilto
A simplified and novel technique to retrieve color images from hand-drawn sketch by human
With the increasing adoption of human-computer interaction, there is a growing trend of extracting the image through hand-drawn sketches by humans to find out correlated objects from the storage unit. A review of the existing system shows the dominant use of sophisticated and complex mechanisms where the focus is more on accuracy and less on system efficiency. Hence, this proposed system introduces a simplified extraction of the related image using an attribution clustering process and a cost-effective training scheme. The proposed method uses K-means clustering and bag-of-attributes to extract essential information from the sketch. The proposed system also introduces a unique indexing scheme that makes the retrieval process faster and results in retrieving the highest-ranked images. Implemented in MATLAB, the study outcome shows the proposed system offers better accuracy and processing time than the existing feature extraction technique
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
- …