11 accepted papers
Segment Anything Models (SAM) achieve impressive universal segmentation performance but require massive datasets (e.g., 11M images) and rely solely on RGB inputs. Recent efficient variants reduce computation but still depend on large-scale training. We propose a lightweight RGB-D fusion framework th…
This work studies a composite minimization problem involving a differentiable function q and a nonsmooth function h, both of which may be nonconvex. This problem is ubiquitous in signal processing and machine learning yet remains challenging to solve efficiently, particularly when large-scale instan…
This work focuses on enhancing computational efficiency in wide-band radio frequency (RF) imaging. Specifically, we aim to efficiently generate multispectral RF images that contain information on range, angle, and frequency-selected responses, employing wide-band frequency-modulated continuous wave…
SLAM systems based on NeRF have demonstrated superior performance in rendering quality and scene reconstruction for static environments compared to traditional dense SLAM. However, they encounter tracking drift and mapping errors in real-world scenarios with dynamic interferences. To address these i
Current Simultaneous Localization and Mapping (SLAM) methods based on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting excel in reconstructing static 3D scenes but struggle with tracking and reconstruction in dynamic environments, such as real-world scenes with moving elements. Existing NeRF-b
This paper addresses gridless direction-of-arrival (DOA) estimation for unknown source signals across a wide frequency range. While processing signals jointly across multiple frequencies can improve DOA estimation accuracy, it significantly increases computational complexity as the model size expand…
Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling (J-DPS) method, which aims to learn optimal selection matrices sim…
We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain,…
This letter details the design and validation of a legged terrestrial locomotion system integrated onto a hummingbird-scale flapping wing robot. Two pairs of legs powered by the flight actuators allow forward, reverse, and steering motions while on the ground. Through controlled sinusoidal drive sig
Insects wings are subject to wear and tear from collisions and environmental disturbances during flight. They can tolerate both symmetrical and asymmetrical wing damages while maintaining flight capability to some extent. Drawing inspiration from nature's adaptation capabilities, we investigated the
Achieving good speed and accuracy trade-off on a target platform is very important in deploying deep neural networks in real world scenarios. However, most existing automatic architecture search approaches only concentrate on high performance. In this work, we propose an algorithm that can offer bet…