Search Results for author:
Found 15 papers, 12 papers with code
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs.
RoDLA: Benchmarking the Robustness of Document Layout Analysis Models
To address this, we are the first to introduce a robustness benchmark for DLA models, which includes 450K document images of three datasets.
Skeleton-Based Human Action Recognition with Noisy Labels
In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark.
MolNexTR: A Generalized Deep Learning Model for Molecular Image Recognition
In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.
Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation
Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework.
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector
Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks.
MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar
In response to these challenges, we propose MonoGaussianAvatar (Monocular Gaussian Point-based Head Avatar), a novel approach that harnesses 3D Gaussian point representation coupled with a Gaussian deformation field to learn explicit head avatars from monocular portrait videos.
Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions
In this paper, we measure their ability to refute popular S&P misconceptions that the general public holds.
Unveiling the Hidden Realm: Self-supervised Skeleton-based Action Recognition in Occluded Environments
To integrate action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions.
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i. e., joints, bones, and motions are used, hence no additional modalities are explored.
Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments
Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images.
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Medical image segmentation is a fundamental task in the community of medical image analysis.
Few Shot Medical Image Segmentation with Cross Attention Transformer
Our proposed network mines the correlations between the support image and query image, limiting them to focus only on useful foreground information and boosting the representation capacity of both the support prototype and query features.
Neural Inertial Localization
This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements.
The Replica Dataset: A Digital Replica of Indoor Spaces
We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale.
