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دسته بندی:
بینایی ماشین - Machine vision
سال انتشار:
2022
عنوان انگلیسی مقاله:
Co-segmentation inspired attention module for video-based computer vision tasks
ترجمه فارسی عنوان مقاله:
ماژول توجه الهام گرفته از تقسیم بندی مشترک برای وظایف بینایی کامپیوتری مبتنی بر ویدئو
منبع:
ScienceDirect- Elsevier- Computer Vision and Image Understanding, 223 (2022) 103532: doi:10:1016/j:cviu:2022:103532
نویسنده:
Arulkumar Subramaniam a,∗, Jayesh Vaidya a, Muhammed Abdul Majeed Ameen a, Athira Nambiar a,b, Anurag Mittal a
چکیده انگلیسی:
Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between
those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing
pre-trained models to perform object detection, object segmentation and/or object pose estimation. Although
using pre-trained models is a viable approach, it has several limitations in the need for an exhaustive annotation
of object categories, a possible domain gap between datasets and a bias that is typically present in pre-trained
models. In this work, we propose to utilize the common rationale that a sequence of video frames capture a
set of common objects and interactions between them, thus a notion of co-segmentation between the video
frame features may equip the model with the ability to automatically focus on task-specific salient regions
and improve the underlying task’s performance in an end-to-end manner. In this regard, we propose a generic
module called ‘‘Co-Segmentation inspired Attention Module’’ (COSAM) that can be plugged in to any CNN
model to promote the notion of co-segmentation based attention among a sequence of video frame features.
We show the application of COSAM in three video-based tasks namely: (1) Video-based person re-ID, (2) Video
captioning, & (3) Video action classification and demonstrate that COSAM is able to capture the task-specific
salient regions in video frames, thus leading to notable performance improvements along with interpretable
attention maps for a variety of video-based vision tasks, with possible application to other video-based vision
tasks as well.
keywords: توجه | تقسیم بندی مشترک | شناسه شخص | زیرنویس ویدیویی | طبقه بندی ویدیویی | Attention | Co-segmentation | Personre-ID | Video-captioning | Video classification
قیمت: رایگان
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