Yan Xiang

Consensus-Aligned Neuron Efficient Fine-Tuning Large Language Models for Multi-Domain Machine Translation

Multi-domain machine translation (MDMT) aims to build a unified model capable of translating content across diverse domains. Despite the impressive machine translation capabilities demonstrated by large language models (LLMs), domain adaptation still remains a challenge for LLMs. Existing MDMT metho

SVA: A Street-View-Aided GNSS Positioning Framework with 2DSDM and Likelihood Road for NLOS/Multipath Mitigation

GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition

Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities. Although recent large language model (LLM)-based methods have demonstrated strong performance, they typically adopt static fusion strategies that integra

Multilingual Generative Retrieval via Cross-lingual Semantic Compression

Generative Information Retrieval is an emerging retrieval paradigm that exhibits remarkable performance in monolingual scenarios. However, applying these methods to multilingual retrieval still encounters two primary challenges, cross-lingual identifier misalignment and identifier inflation. To addr

SVA: A Street-View-Aided GNSS Positioning Framework With 2DSDM and Likelihood Road for NLOS/Multipath Mitigation

Global Navigation Satellite System (GNSS) suffers severe accuracy degradation in urban environments due to Non-Line-of-Sight (NLOS) and multipath effects. Several methods have been proposed to detect and mitigate NLOS/multipath, but those rely on additional equipment, high costs, and limited multipa

THE-SEAN: A Heart Rate Variation-Inspired Temporally High-Order Event-Based Visual Odometry with Self-Supervised Spiking Event Accumulation Networks

Event-based visual odometry has recently gained attention for its high accuracy and real-time performance in fast-motion systems. Unlike traditional synchronous estimators that rely on constant-frequency (zero-order) triggers, event-based visual odometry can actively accumulate information to genera

SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-Time Performance on Mobile Device

With the rapid development of AI hardware accelerators, applying deep learning-based algorithms to solve various low-level vision tasks on mobile devices has gradually become possible. However, two main problems still need to be solved. Firstly, most low-level vision algorithms are task-specific and…

Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation

Multi-modal neural machine translation (MNMT) aims to improve textual level machine translation performance in the presence of text-related images. Most of the previous works on MNMT focus on multi-modal fusion methods with full visual features. However, text and its corresponding image may not matc…

P${3}$-VINS: Tightly-Coupled PPP/INS/Visual SLAM Based on Optimization Approach

Precise Point Positioning (PPP), a cutting edge GNSS technology, can achieve high-precision positioning without base station assistance. Visual-Inertial Odometry (VIO) realizes a more robust local pose estimation than Visual-SLAM. Based on PPP and VIO, we propose a tightly-coupled PPP/INS/Visual SLA