247 research outputs found
Face Clustering for Connection Discovery from Event Images
Social graphs are very useful for many applications, such as recommendations
and community detections. However, they are only accessible to big social
network operators due to both data availability and privacy concerns. Event
images also capture the interactions among the participants, from which social
connections can be discovered to form a social graph. Unlike online social
graphs, social connections carried by event images can be extracted without
user inputs, and hence many social graph-based applications become possible,
even without access to online social graphs. This paper proposes a system to
discover social connections from event images. By utilizing the social
information from even images, such as co-occurrence, a face clustering method
is proposed and implemented, and connections can be discovered without the
identity of the event participants. By collecting over 40000 faces from over
3000 participants, it is shown that the faces can be well clustered with 80% in
F1 score, and social graphs can be constructed. Utilizing offline event images
may create a long-term impact on social network analytics.Comment: 18 page
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work
Inspired by the fact that human brains can emphasize discriminative parts of
the input and suppress irrelevant ones, substantial local mechanisms have been
designed to boost the development of computer vision. They can not only focus
on target parts to learn discriminative local representations, but also process
information selectively to improve the efficiency. In terms of application
scenarios and paradigms, local mechanisms have different characteristics. In
this survey, we provide a systematic review of local mechanisms for various
computer vision tasks and approaches, including fine-grained visual
recognition, person re-identification, few-/zero-shot learning, multi-modal
learning, self-supervised learning, Vision Transformers, and so on.
Categorization of local mechanisms in each field is summarized. Then,
advantages and disadvantages for every category are analyzed deeply, leaving
room for exploration. Finally, future research directions about local
mechanisms have also been discussed that may benefit future works. To the best
our knowledge, this is the first survey about local mechanisms on computer
vision. We hope that this survey can shed light on future research in the
computer vision field
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