464 research outputs found
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows
both text and sketch as input modalities for the query. A cross-modal deep
network architecture is formulated to jointly model the sketch and text input
modalities as well as the the image output modality, learning a common
embedding between text and images and between sketches and images. In addition,
an attention model is used to selectively focus the attention on the different
objects of the image, allowing for retrieval with multiple objects in the
query. Experiments show that the proposed method performs the best in both
single and multiple object image retrieval in standard datasets.Comment: Accepted at ICPR 201
Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval
In this paper, we investigate the problem of zero-shot sketch-based image
retrieval (ZS-SBIR), where human sketches are used as queries to conduct
retrieval of photos from unseen categories. We importantly advance prior arts
by proposing a novel ZS-SBIR scenario that represents a firm step forward in
its practical application. The new setting uniquely recognizes two important
yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap
between amateur sketch and photo, and (ii) the necessity for moving towards
large-scale retrieval. We first contribute to the community a novel ZS-SBIR
dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000
photos spanning across 110 categories. Highly abstract amateur human sketches
are purposefully sourced to maximize the domain gap, instead of ones included
in existing datasets that can often be semi-photorealistic. We then formulate a
ZS-SBIR framework to jointly model sketches and photos into a common embedding
space. A novel strategy to mine the mutual information among domains is
specifically engineered to alleviate the domain gap. External semantic
knowledge is further embedded to aid semantic transfer. We show that, rather
surprisingly, retrieval performance significantly outperforms that of
state-of-the-art on existing datasets that can already be achieved using a
reduced version of our model. We further demonstrate the superior performance
of our full model by comparing with a number of alternatives on the newly
proposed dataset. The new dataset, plus all training and testing code of our
model, will be publicly released to facilitate future researchComment: Oral paper in CVPR 201
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
Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval
Human sketches are unique in being able to capture both the spatial topology of a visual object, as well as its subtle appearance details. Fine-grained sketch-based image retrieval (FG-SBIR) importantly leverages on such fine-grained characteristics of sketches to conduct instance-level retrieval of photos. Nevertheless, human sketches are often highly abstract and iconic, resulting in severe misalignments with candidate photos which in turn make subtle visual detail matching difficult. Existing FG-SBIR approaches focus only on coarse holistic matching via deep cross-domain representation learning, yet ignore explicitly accounting for fine-grained details and their spatial context. In this paper, a novel deep FG-SBIR model is proposed which differs significantly from the existing models in that: (1) It is spatially aware, achieved by introducing an attention module that is sensitive to the spatial position of visual details: (2) It combines coarse and fine semantic information via a shortcut connection fusion block: and (3) It models feature correlation and is robust to misalignments between the extracted features across the two domains by introducing a novel higher-order learnable energy function (HOLEF) based loss. Extensive experiments show that the proposed deep spatial-semantic attention model significantly outperforms the state-of-the-art
Deep Learning for Free-Hand Sketch: A Survey
Free-hand sketches are highly illustrative, and have been widely used by
humans to depict objects or stories from ancient times to the present. The
recent prevalence of touchscreen devices has made sketch creation a much easier
task than ever and consequently made sketch-oriented applications increasingly
popular. The progress of deep learning has immensely benefited free-hand sketch
research and applications. This paper presents a comprehensive survey of the
deep learning techniques oriented at free-hand sketch data, and the
applications that they enable. The main contents of this survey include: (i) A
discussion of the intrinsic traits and unique challenges of free-hand sketch,
to highlight the essential differences between sketch data and other data
modalities, e.g., natural photos. (ii) A review of the developments of
free-hand sketch research in the deep learning era, by surveying existing
datasets, research topics, and the state-of-the-art methods through a detailed
taxonomy and experimental evaluation. (iii) Promotion of future work via a
discussion of bottlenecks, open problems, and potential research directions for
the community.Comment: This paper is accepted by IEEE TPAM
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
- …