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Found 10 papers, 6 papers with code
A Bi-Pyramid Multimodal Fusion Method for the Diagnosis of Bipolar Disorders
Previous research on the diagnosis of Bipolar disorder has mainly focused on resting-state functional magnetic resonance imaging.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
Multi-Dimension-Embedding-Aware Modality Fusion Transformer for Psychiatric Disorder Clasification
In this work, we proposed a multi-dimension-embedding-aware modality fusion transformer (MFFormer) for schizophrenia and bipolar disorder classification using rs-fMRI and T1 weighted structural MRI (T1w sMRI).
Temporal Action Localization with Enhanced Instant Discriminability
Temporal action detection (TAD) aims to detect all action boundaries and their corresponding categories in an untrimmed video.
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Temporal Action Localization
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Tracker Meets Night: A Transformer Enhancer for UAV Tracking
To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches.
ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones
To this concern, this work proposes a real-time augmented reality virtual shoe try-on system for smartphones, namely ARShoe.
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems
Estimating a scene's depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field.
Fast Monocular Hand Pose Estimation on Embedded Systems
Hand pose estimation is a fundamental task in many human-robot interaction-related applications.
Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs
In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications. This article proposes an appearance-based loop closure detection pipeline named ``FILD++" (Fast and Incremental Loop closure Detection). First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted. Subsequently, a hierarchical navigable small-world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features. Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route. An image-to-image pairing follows, which exploits local features to evaluate the spatial information.
Loop Closure Detection
Simultaneous Localization and Mapping
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
