1,576 research outputs found
Deep Multi-task Attribute-driven Ranking for Fine-grained Sketch-based Image Retrieval
Fine-grained sketch-based image retrieval (SBIR) aims to go beyond conventional SBIR to perform instance-level cross-domain retrieval: finding the specific photo that matches an input sketch. Existing methods focus on designing/learning good features for cross-domain matching and/or learning cross-domain matching functions. However, they neglect the semantic aspect of retrieval, i.e., what meaningful object properties does a user try encode in her/his sketch? We propose a fine-grained SBIR model that exploits semantic attributes and deep feature learning in a complementary way. Specifically, we perform multi-task deep learning with three objectives, including: retrieval by fine-grained ranking on a learned representation, attribute prediction, and attribute-level ranking. Simultaneously predicting semantic attributes and using such predictions in the ranking procedure help retrieval results to be more semantically relevant. Importantly, the introduction of semantic attribute learning in the model allows for the elimination of the otherwise prohibitive cost of human annotations required for training a fine-grained deep ranking model. Experimental results demonstrate that our method outperforms the state-of-the-art on challenging fine-grained SBIR benchmarks while requiring less annotation
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 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
Sketch Me That Shoe
This project received support from the European Union’s Horizon 2020 research and innovation programme under grant agreement #640891, the Royal Society and Natural Science Foundation of China (NSFC) joint grant #IE141387 and #61511130081, and the China Scholarship Council (CSC). We gratefully acknowledge the support of NVIDIA Corporation for the donation of the GPUs used for this research
Fine-Grained Image Analysis with Deep Learning: A Survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem
in computer vision and pattern recognition, and underpins a diverse set of
real-world applications. The task of FGIA targets analyzing visual objects from
subordinate categories, e.g., species of birds or models of cars. The small
inter-class and large intra-class variation inherent to fine-grained image
analysis makes it a challenging problem. Capitalizing on advances in deep
learning, in recent years we have witnessed remarkable progress in deep
learning powered FGIA. In this paper we present a systematic survey of these
advances, where we attempt to re-define and broaden the field of FGIA by
consolidating two fundamental fine-grained research areas -- fine-grained image
recognition and fine-grained image retrieval. In addition, we also review other
key issues of FGIA, such as publicly available benchmark datasets and related
domain-specific applications. We conclude by highlighting several research
directions and open problems which need further exploration from the community.Comment: Accepted by IEEE TPAM
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
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