1,663 research outputs found
Parsing Objects at a Finer Granularity: A Survey
Fine-grained visual parsing, including fine-grained part segmentation and
fine-grained object recognition, has attracted considerable critical attention
due to its importance in many real-world applications, e.g., agriculture,
remote sensing, and space technologies. Predominant research efforts tackle
these fine-grained sub-tasks following different paradigms, while the inherent
relations between these tasks are neglected. Moreover, given most of the
research remains fragmented, we conduct an in-depth study of the advanced work
from a new perspective of learning the part relationship. In this perspective,
we first consolidate recent research and benchmark syntheses with new
taxonomies. Based on this consolidation, we revisit the universal challenges in
fine-grained part segmentation and recognition tasks and propose new solutions
by part relationship learning for these important challenges. Furthermore, we
conclude several promising lines of research in fine-grained visual parsing for
future research.Comment: Survey for fine-grained part segmentation and object recognition;
Accepted by Machine Intelligence Research (MIR
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
A key challenge in fine-grained recognition is how to find and represent
discriminative local regions. Recent attention models are capable of learning
discriminative region localizers only from category labels with reinforcement
learning. However, not utilizing any explicit part information, they are not
able to accurately find multiple distinctive regions. In this work, we
introduce an attribute-guided attention localization scheme where the local
region localizers are learned under the guidance of part attribute
descriptions. By designing a novel reward strategy, we are able to learn to
locate regions that are spatially and semantically distinctive with
reinforcement learning algorithm. The attribute labeling requirement of the
scheme is more amenable than the accurate part location annotation required by
traditional part-based fine-grained recognition methods. Experimental results
on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme
on both fine-grained recognition and attribute recognition
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
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