1,005 research outputs found
Bidirectional Propagation for Cross-Modal 3D Object Detection
Recent works have revealed the superiority of feature-level fusion for
cross-modal 3D object detection, where fine-grained feature propagation from 2D
image pixels to 3D LiDAR points has been widely adopted for performance
improvement. Still, the potential of heterogeneous feature propagation between
2D and 3D domains has not been fully explored. In this paper, in contrast to
existing pixel-to-point feature propagation, we investigate an opposite
point-to-pixel direction, allowing point-wise features to flow inversely into
the 2D image branch. Thus, when jointly optimizing the 2D and 3D streams, the
gradients back-propagated from the 2D image branch can boost the representation
ability of the 3D backbone network working on LiDAR point clouds. Then,
combining pixel-to-point and point-to-pixel information flow mechanisms, we
construct an bidirectional feature propagation framework, dubbed BiProDet. In
addition to the architectural design, we also propose normalized local
coordinates map estimation, a new 2D auxiliary task for the training of the 2D
image branch, which facilitates learning local spatial-aware features from the
image modality and implicitly enhances the overall 3D detection performance.
Extensive experiments and ablation studies validate the effectiveness of our
method. Notably, we rank on the highly competitive
KITTI benchmark on the cyclist class by the time of submission. The source code
is available at https://github.com/Eaphan/BiProDet.Comment: Accepted by ICLR2023. Code is avaliable at
https://github.com/Eaphan/BiProDe
Machine Learning for Microcontroller-Class Hardware -- A Review
The advancements in machine learning opened a new opportunity to bring
intelligence to the low-end Internet-of-Things nodes such as microcontrollers.
Conventional machine learning deployment has high memory and compute footprint
hindering their direct deployment on ultra resource-constrained
microcontrollers. This paper highlights the unique requirements of enabling
onboard machine learning for microcontroller class devices. Researchers use a
specialized model development workflow for resource-limited applications to
ensure the compute and latency budget is within the device limits while still
maintaining the desired performance. We characterize a closed-loop widely
applicable workflow of machine learning model development for microcontroller
class devices and show that several classes of applications adopt a specific
instance of it. We present both qualitative and numerical insights into
different stages of model development by showcasing several use cases. Finally,
we identify the open research challenges and unsolved questions demanding
careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa
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