Search Results for author: Yiran Shen

Found 10 papers, 6 papers with code

A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models

no code implementations9 Apr 2025 Zhouhang Xie, Junda Wu, Yiran Shen, Yu Xia, Xintong Li, Aaron Chang, Ryan Rossi, Sachin Kumar, Bodhisattwa Prasad Majumder, Jingbo Shang, Prithviraj Ammanabrolu, Julian McAuley

Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization.

Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking

no code implementations11 Mar 2025 Xucheng Guo, Yiran Shen, Xiaofang Xiao, Yuanfeng Zhou, Lin Wang

Ev-Layout makes key contributions to the community by: Utilizing a hybrid data collection platform (with a head-mounted display and VR interface) that integrates both RGB and bio-inspired event cameras to capture indoor layouts in motion.

Benchmarking

NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References

1 code implementation11 Jan 2025 Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Weidong Cai, Tongliang Liu

Neural View Synthesis (NVS), such as NeRF and 3D Gaussian Splatting, effectively creates photorealistic scenes from sparse viewpoints, typically evaluated by quality assessment methods like PSNR, SSIM, and LPIPS.

NeRF Representation Learning +2

NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods

1 code implementation11 Dec 2024 Qiang Qu, Hanxue Liang, Xiaoming Chen, Yuk Ying Chung, Yiran Shen

To address the issues above, we propose NeRF-NQA, the first no-reference quality assessment method for densely-observed scenes synthesized from the NVS and NeRF variants.

NeRF SSIM

EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision

1 code implementation10 Dec 2024 Qiang Qu, Xiaoming Chen, Yuk Ying Chung, Yiran Shen

However, most of the state-of-the-art event-stream representations are manually designed and the quality of these representations cannot be guaranteed due to the noisy nature of event-streams.

Event-based vision Optical Flow Estimation +1

PDSR: A Privacy-Preserving Diversified Service Recommendation Method on Distributed Data

no code implementations28 Aug 2024 Lina Wang, Huan Yang, Yiran Shen, Chao Liu, Lianyong Qi, Xiuzhen Cheng, Feng Li

Therefore, to enable data sharing across the different platforms for diversified service recommendation, we propose a Privacy-preserving Diversified Service Recommendation (PDSR) method.

Collaborative Filtering Diversity +1

E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning

1 code implementation16 Jan 2024 Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu

In this work, we propose \textbf{E2HQV}, a novel E2V paradigm designed to produce high-quality video frames from events.

Video Generation

Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution

1 code implementation1 Jan 2024 Zeke Zexi Hu, Xiaoming Chen, Vera Yuk Ying Chung, Yiran Shen

The effective extraction of spatial-angular features plays a crucial role in light field image super-resolution (LFSR) tasks, and the introduction of convolution and Transformers leads to significant improvement in this area.

Image Super-Resolution

High Speed Rotation Estimation with Dynamic Vision Sensors

no code implementations6 Sep 2022 Guangrong Zhao, Yiran Shen, Ning Chen, Pengfei Hu, Lei Liu, Hongkai Wen

By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets.

Vocal Bursts Intensity Prediction

Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning

1 code implementation10 Dec 2019 Chris Xiaoxuan Lu, Bowen Du, Hongkai Wen, Sen Wang, Andrew Markham, Ivan Martinovic, Yiran Shen, Niki Trigoni

Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms.

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