2,820 research outputs found
Transformer for Object Re-Identification: A Survey
Object Re-Identification (Re-ID) aims to identify and retrieve specific
objects from varying viewpoints. For a prolonged period, this field has been
predominantly driven by deep convolutional neural networks. In recent years,
the Transformer has witnessed remarkable advancements in computer vision,
prompting an increasing body of research to delve into the application of
Transformer in Re-ID. This paper provides a comprehensive review and in-depth
analysis of the Transformer-based Re-ID. In categorizing existing works into
Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal
Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages
demonstrated by the Transformer in addressing a multitude of challenges across
these domains. Considering the trending unsupervised Re-ID, we propose a new
Transformer baseline, UntransReID, achieving state-of-the-art performance on
both single-/cross modal tasks. Besides, this survey also covers a wide range
of Re-ID research objects, including progress in animal Re-ID. Given the
diversity of species in animal Re-ID, we devise a standardized experimental
benchmark and conduct extensive experiments to explore the applicability of
Transformer for this task to facilitate future research. Finally, we discuss
some important yet under-investigated open issues in the big foundation model
era, we believe it will serve as a new handbook for researchers in this field
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
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