Jing Jin

COBRA: Contribution-Based Bayesian Rank Allocation for Parameter-Efficient Fine-Tuning

Full fine-tuning of large language models (LLMs) incurs prohibitive computational and storage costs. Parameter-efficient fine-tuning (PEFT) addresses this limitation, with Low-Rank Adaptation (LoRA) gaining widespread adoption due to its simplicity and zero inference overhead. However, LoRA and its …

EGS-SLAM: RGB-D Gaussian Splatting SLAM with Events

EGS-SLAM: RGB-D Gaussian Splatting SLAM With Events

Gaussian Splatting SLAM (GS-SLAM) offers a notable improvement over traditional SLAM methods, in enabling photorealistic 3D reconstruction that conventional approaches often struggle to achieve. However, existing GS-SLAM systems perform poorly under persistent and severe motion blur commonly encount

LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation

Recent studies have demonstrated that large language models (LLMs) exhibit significant biases in evaluation tasks, particularly in preferentially rating and favoring self-generated content. However, the extent to which this bias manifests in fact-oriented tasks, especially within retrieval-augmented…

DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering

Large language models (LLMs) are widely used in question-answering (QA) systems but often generate information with hallucinations. Retrieval-augmented generation (RAG) offers a potential remedy, yet the uneven retrieval quality and irrelevant contents may distract LLMs.In this work, we address thes…

Learning-Based Multimodal Control for a Supernumerary Robotic System in Human-Robot Collaborative Sorting

In this letter, a multi-modal learning and control framework is proposed for the control of a supernumerary robotic limb (SRL). The SRL is a wearable robotic arm designed to enhance the manipulation capabilities of its human user and extend the workspace by reaching greater heights. The multi-modal

SO-Net: Model-Agnostic Sequential Hand Pose Optimization Framework

Hand Pose Estimation (HPE) is a crucial technique for human-computer interaction perception. Recent works have shown that leveraging temporal information yields significant importance in the stability of the HPE system. However, existing sequential optimization methods are mainly designed for specif…

Select High-quality Synthetic QA Pairs to Augment Training Data in MRC under the Reward Guidance of Generative Language Models

Synthesizing QA pairs via question generator (QG) for data augmentation is widely used in Machine Reading Comprehension (MRC), especially in data-scarce scenarios like limited labeled data or domain adaptation. However, the quality of generated QA pairs varies, and it is necessary to select the ones…

Learning Dynamic Interpolation for Extremely Sparse Light Fields With Wide Baselines

In this paper, we tackle the problem of dense light field (LF) reconstruction from sparsely-sampled ones with wide baselines and propose a learnable model, namely dynamic interpolation, to replace the commonly-used geometry warping operation. Specifically, with the estimated geometric relation betwe…

Deep Spatial-angular Regularization for Compressive Light Field Reconstruction over Coded Apertures

Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithms. The bottleneck lies in the reconstruction algorithms, resulting in rather limited recons…

Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization

Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension. LF spatial super-resolution (SR) thus becomes an indispensable part of the LF camera processing pipeline. The high-dimensio…

Classifier Cascade to Aid in Detection of Epileptiform Transients in Interictal EEG

The presence of Epileptiform Transients (ET) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. Automated ET detection can increase the uniformity and speed of ET detection. Current ET detection methods suffer from insufficient precision an…

Clustering of interictal spikes by dynamic time warping and affinity propagation

Epilepsy is often associated with the presence of spikes in electroencephalograms (EEGs). The spike waveforms vary vastly among epilepsy patients, and also for the same patient across time. In order to develop semi-automated and automated methods for detecting spikes, it is crucial to obtain a bette…

Epileptiform spike detection via convolutional neural networks

The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated fashion…

Fast and efficient rejection of background waveforms in interictal EEG

Automated annotation of electroencephalograms (EEG) of epileptic patients is important in diagnosis and management of epilepsy. Epilepsy is often associated with the presence of epileptiform transients (ET) in the EEG. To develop an efficient ET detector, a vast amount of data is required to train a…

Removal of EEG artifacts for BCI applications using fully Bayesian tensor completion

High accuracy of electroencephalogram (EEG) classification can hardly be achieved if the signals are contaminated by severe artefacts. One helpless way to avoid such artefacts is usually to directly discard the severely disturbed EEG segments. This study considers a more elegant way that tries to re…