3,335 research outputs found
Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network
Recently, change detection methods for synthetic aperture radar (SAR) images
based on convolutional neural networks (CNN) have gained increasing research
attention. However, existing CNN-based methods neglect the interactions among
multilayer convolutions, and errors involved in the preclassification restrict
the network optimization. To this end, we proposed a layer attention-based
noise-tolerant network, termed LANTNet. In particular, we design a layer
attention module that adaptively weights the feature of different convolution
layers. In addition, we design a noise-tolerant loss function that effectively
suppresses the impact of noisy labels. Therefore, the model is insensitive to
noisy labels in the preclassification results. The experimental results on
three SAR datasets show that the proposed LANTNet performs better compared to
several state-of-the-art methods. The source codes are available at
https://github.com/summitgao/LANTNetComment: Accepted by IEEE Geoscience and Remote Sensing Letters (GRSL) 2022,
code is available at https://github.com/summitgao/LANTNe
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
Multi-object tracking (MOT) is a fundamental problem in computer vision with
numerous applications, such as intelligent surveillance and automated driving.
Despite the significant progress made in MOT, pedestrian attributes, such as
gender, hairstyle, body shape, and clothing features, which contain rich and
high-level information, have been less explored. To address this gap, we
propose a simple, effective, and generic method to predict pedestrian
attributes to support general Re-ID embedding. We first introduce AttMOT, a
large, highly enriched synthetic dataset for pedestrian tracking, containing
over 80k frames and 6 million pedestrian IDs with different time, weather
conditions, and scenarios. To the best of our knowledge, AttMOT is the first
MOT dataset with semantic attributes. Subsequently, we explore different
approaches to fuse Re-ID embedding and pedestrian attributes, including
attention mechanisms, which we hope will stimulate the development of
attribute-assisted MOT. The proposed method AAM demonstrates its effectiveness
and generality on several representative pedestrian multi-object tracking
benchmarks, including MOT17 and MOT20, through experiments on the AttMOT
dataset. When applied to state-of-the-art trackers, AAM achieves consistent
improvements in MOTA, HOTA, AssA, IDs, and IDF1 scores. For instance, on MOT17,
the proposed method yields a +1.1 MOTA, +1.7 HOTA, and +1.8 IDF1 improvement
when used with FairMOT. To encourage further research on attribute-assisted
MOT, we will release the AttMOT dataset
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