8,742 research outputs found
Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
Anticipating human intention by observing one's actions has many
applications. For instance, picking up a cellphone, then a charger (actions)
implies that one wants to charge the cellphone (intention). By anticipating the
intention, an intelligent system can guide the user to the closest power
outlet. We propose an on-wrist motion triggered sensing system for anticipating
daily intentions, where the on-wrist sensors help us to persistently observe
one's actions. The core of the system is a novel Recurrent Neural Network (RNN)
and Policy Network (PN), where the RNN encodes visual and motion observation to
anticipate intention, and the PN parsimoniously triggers the process of visual
observation to reduce computation requirement. We jointly trained the whole
network using policy gradient and cross-entropy loss. To evaluate, we collect
the first daily "intention" dataset consisting of 2379 videos with 34
intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%,
97.56% accuracy on three users while processing only 29% of the visual
observation on average
An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary
The precise segmentation of ore images is critical to the successful
execution of the beneficiation process. Due to the homogeneous appearance of
the ores, which leads to low contrast and unclear boundaries, accurate
segmentation becomes challenging, and recognition becomes problematic. This
paper proposes a lightweight framework based on Multi-Layer Perceptron (MLP),
which focuses on solving the problem of edge burring. Specifically, we
introduce a lightweight backbone better suited for efficiently extracting
low-level features. Besides, we design a feature pyramid network consisting of
two MLP structures that balance local and global information thus enhancing
detection accuracy. Furthermore, we propose a novel loss function that guides
the prediction points to match the instance edge points to achieve clear object
boundaries. We have conducted extensive experiments to validate the efficacy of
our proposed method. Our approach achieves a remarkable processing speed of
over 27 frames per second (FPS) with a model size of only 73 MB. Moreover, our
method delivers a consistently high level of accuracy, with impressive
performance scores of 60.4 and 48.9 in~ and~
respectively, as compared to the currently available state-of-the-art
techniques, when tested on the ore image dataset. The source code will be
released at \url{https://github.com/MVME-HBUT/ORENEXT}.Comment: 10 pages, 8 figure
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