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Found 8 papers, 4 papers with code
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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FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model.
Network resilience in the aging brain
Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.
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.
Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss
In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data.
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
