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Found 20 papers, 8 papers with code
RIS-Assisted Passive Localization (RAPL): An Efficient Zero-Overhead Framework Using Conditional Sample Mean
The proposed method includes two stages, the angle-of-directions (AoDs) between the RISs and the user is estimated by using the conditional sample mean in the first stage, and then the user's position is determined based on the estimated multiple AoD pairs in the second stage.
RIS-Assisted Localization: A Novel Conditional Sample Mean Approach without CSI
Reconfigurable intelligent surface (RIS) has been recognized as a promising solution for enhancing localization accuracy.
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems
As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies.
Enhancing Multimodal Large Language Models Complex Reason via Similarity Computation
Multimodal large language models have experienced rapid growth, and numerous different models have emerged.
Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
Multiple clustering aims to discover various latent structures of data from different aspects.
Swift Sampler: Efficient Learning of Sampler by 10 Parameters
In particular, \textbf{SS} utilizes a novel formulation to map a sampler to a low dimension of hyper-parameters and uses an approximated local minimum to quickly examine the quality of a sampler.
Text-Guided Mixup Towards Long-Tailed Image Categorization
In many real-world applications, the frequency distribution of class labels for training data can exhibit a long-tailed distribution, which challenges traditional approaches of training deep neural networks that require heavy amounts of balanced data.
SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors
3D Gaussian Splatting (3DGS) demonstrates unparalleled superior performance in 3D scene reconstruction.
Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering
Traditionally, aligning a user's brief keyword of interest with the corresponding vision components was challenging, but the emergence of multi-modal and large language models (LLMs) has begun to bridge this gap.
Dual-disentangled Deep Multiple Clustering
In the E-step, the disentanglement learning module employs coarse-grained and fine-grained disentangled representations to obtain a more diverse set of latent factors from the data.
Building Lane-Level Maps from Aerial Images
In this paper, we introduce for the first time a large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road.
DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation
The task of 3D semantic scene completion using monocular cameras is gaining significant attention in the field of autonomous driving.
HarmonicNeRF: Geometry-Informed Synthetic View Augmentation for 3D Scene Reconstruction in Driving Scenarios
In the realm of autonomous driving, achieving precise 3D reconstruction of the driving environment is critical for ensuring safety and effective navigation.
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied Contexts
The dynamic nature of real-world environments necessitates the use of dynamic query mechanisms in 3D object detection to adaptively capture and process the complex spatio-temporal relationships present in these scenes.
NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space
Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single image, requiring no 3D inputs.
3D Semantic Scene Completion from a single 2D image
3D Semantic Scene Completion from a single RGB image
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Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN
To explore the differences between Transformers and CNNs, we employ a sparse pixel approach to contrastively analyze the distinctions between the two.
AugDMC: Data Augmentation Guided Deep Multiple Clustering
Thereafter, multiple clusterings based on different aspects of the data can be obtained.
Context-Aware Feature Learning for Noise Robust Person Search
For human-introduced noise, we propose a noise-discovery and noise-suppression training process for mislabeling robust person search.
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors
Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting.
Sufficient Forecasting Using Factor Models
Our method and theory allow the number of predictors to be larger than the number of observations.
