Search Results for author:
Found 68 papers, 26 papers with code
GSOT3D: Towards Generic 3D Single Object Tracking in the Wild
In this paper, we present a novel benchmark, GSOT3D, that aims at facilitating development of generic 3D single object tracking (SOT) in the wild.
Advancing Large Language Model Attribution through Self-Improving
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems.
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
News summarization in today's global scene can be daunting with its flood of multilingual content and varied viewpoints from different sources.
Extending Context Window of Large Language Models from a Distributional Perspective
However, existing scaling methods often rely on empirical approaches and lack a profound understanding of the internal distribution within RoPE, resulting in suboptimal performance in extending the context window length.
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
To this end, we introduce an efficient structured pruning framework named CFSP, which leverages both Coarse (interblock) and Fine-grained (intrablock) activation information as an importance criterion to guide pruning.
SEE: Semantically Aligned EEG-to-Text Translation
Decoding neurophysiological signals into language is of great research interest within brain-computer interface (BCI) applications.
HEAD: A Bandwidth-Efficient Cooperative Perception Approach for Heterogeneous Connected and Autonomous Vehicles
In cooperative perception studies, there is often a trade-off between communication bandwidth and perception performance.
A Survey on Reinforcement Learning Applications in SLAM
The emergence of mobile robotics, particularly in the automotive industry, introduces a promising era of enriched user experiences and adept handling of complex navigation challenges.
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling
New candidate tokens from the decoding process are then used to update the matrix.
Bucket Pre-training is All You Need
Large language models (LLMs) have demonstrated exceptional performance across various natural language processing tasks.
Review-LLM: Harnessing Large Language Models for Personalized Review Generation
Finally, we feed the prompt text into LLMs, and use Supervised Fine-Tuning (SFT) to make the model generate personalized reviews for the given user and target item.
Automated Progressive Red Teaming
To address this gap, we propose Automated Progressive Red Teaming (APRT) as an effectively learnable framework.
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
To address this problem, we propose the Make Some Noise (MSN) training framework as a replacement for the supervised fine-tuning stage of the large language model.
LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking
In this paper, we aim to mitigate such information loss to boost the performance of the low-resolution Transformer tracking via dual knowledge distillation from a frozen high-resolution (but not a larger) Transformer tracker.
Ranked #25 on
Visual Object Tracking
on LaSOT
MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability
When encountering malicious instructions, the router will assign a higher weight to the safe LLM to ensure that responses are harmless.
T3RD: Test-Time Training for Rumor Detection on Social Media
To tackle this challenge, we introduce the Test-Time Training for Rumor Detection (T^3RD) to enhance the performance of rumor detection models on low-resource datasets.
Dynamic Prompt Optimizing for Text-to-Image Generation
Users assign weights or alter the injection time steps of certain words in the text prompts to improve the quality of generated images.
Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, we subsequently add a modal alignment module to line up a dominant modality (e. g., PET) with representations of auxiliary modalities (MR).
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
The results show that our approach not only boosts the general reasoning performance of LLMs but also makes considerable strides towards their capacity for abstract reasoning, moving beyond simple memorization or imitation to a more nuanced understanding and application of generic facts.
Skipformer: A Skip-and-Recover Strategy for Efficient Speech Recognition
Conformer-based attention models have become the de facto backbone model for Automatic Speech Recognition tasks.
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers
Through analysis, we found the contribution ratio of Multi-Head Attention (a combination function) to pre-trained language modeling is a key factor affecting base capabilities.
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated).
Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion
Early infancy is a rapid and dynamic neurodevelopmental period for behavior and neurocognition.
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts
In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models
The ability to fine-tune generative models for text-to-image generation tasks is crucial, particularly facing the complexity involved in accurately interpreting and visualizing textual inputs.
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models
Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection module to pick out the corresponding one for the testing input, aiming at handling the challenges of catastrophic forgetting and knowledge transfer in CL.
CAMEL: CAusal Motion Enhancement Tailored for Lifting Text-driven Video Editing
Second to enhance motion coherence and extend the generalization of appearance content to creative textual prompts we propose the causal motion-enhanced attention mechanism.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities.
VirtualPainting: Addressing Sparsity with Virtual Points and Distance-Aware Data Augmentation for 3D Object Detection
In recent times, there has been a notable surge in multimodal approaches that decorates raw LiDAR point clouds with camera-derived features to improve object detection performance.
Self-Supervised Disentangled Representation Learning for Robust Target Speech Extraction
However, in the task of target speech extraction, certain elements of global and local semantic information in the reference speech, which are irrelevant to speaker identity, can lead to speaker confusion within the speech extraction network.
SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
Ranked #4 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information
According to the survival analysis results on 14 cancer types, Gene-MOE outperformed state-of-the-art models on 12 cancer types.
Key Frame Mechanism For Efficient Conformer Based End-to-end Speech Recognition
Following the initial encoder, we introduce an intermediate CTC loss function to compute the label frame, enabling us to extract the key frames and blank frames for KFSA.
UAV-assisted Semantic Communication with Hybrid Action Reinforcement Learning
In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas.
Hierarchical Semantic Perceptual Listener Head Video Generation: A High-performance Pipeline
In dyadic speaker-listener interactions, the listener's head reactions along with the speaker's head movements, constitute an important non-verbal semantic expression together.
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models
Despite achieving remarkable performance on various vision-language tasks, Transformer-based Vision-Language Models (VLMs) suffer from redundancy in inputs and parameters, significantly hampering their efficiency in real-world applications.
CGCE: A Chinese Generative Chat Evaluation Benchmark for General and Financial Domains
Generative chat models, such as ChatGPT and GPT-4, have revolutionized natural language generation (NLG) by incorporating instructions and human feedback to achieve significant performance improvements.
Self-QA: Unsupervised Knowledge Guided Language Model Alignment
Large-scale language models like ChatGPT and GPT-4 have gained attention for their impressive conversational and generative capabilities.
XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
In recent years, pre-trained language models have undergone rapid development with the emergence of large-scale models.
Towards Causal Representation Learning and Deconfounding from Indefinite Data
We implement the above designs as a dynamic variational inference model, tailored to learn causal representation from indefinite data under latent confounding.
PUNR: Pre-training with User Behavior Modeling for News Recommendation
Firstly, we introduce the user behavior masking pre-training task to recover the masked user behaviors based on their contextual behaviors.
PlanarTrack: A Large-scale Challenging Benchmark for Planar Object Tracking
In addition, we further derive a variant named PlanarTrack$_{\mathbf{BB}}$ for generic object tracking from PlanarTrack.
UAV aided Metaverse over Wireless Communications: A Reinforcement Learning Approach
A huge amount of data in physical world needs to be synchronized to the virtual world to provide immersive experience for users, and there will be higher requirements on coverage to include more users into Metaverse.
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for edge and cloud collaborative learning.
Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NAS
To address this problem, we propose a step-by-step training super-net scheme from one-shot NAS to few-shot NAS.
RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem
In this way, the search information can be effectively used by the reinforcement learning method.
An Empirical Study on Disentanglement of Negative-free Contrastive Learning
In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empirically.
TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation
Knowledge graph embedding (KGE) aims to learn continuous vectors of relations and entities in knowledge graph.
Ranked #8 on
Link Property Prediction
on ogbl-wikikg2
Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks
This study for the first time considers both types of risk and their joint effects in bankruptcy prediction.
Online Self-Evolving Anomaly Detection in Cloud Computing Environments
As a distinct advantage of our framework, cloud system administrators only need to check a small number of detected anomalies, and their decisions are leveraged to update the detector.
Revisit Dictionary Learning for Video Compressive Sensing under the Plug-and-Play Framework
Aiming at high-dimensional (HD) data acquisition and analysis, snapshot compressive imaging (SCI) obtains the 2D compressed measurement of HD data with optical imaging systems and reconstructs HD data using compressive sensing algorithms.
Disentangling Properties of Contrastive Methods
In this paper, we explored the possibility of using contrastive methods to learn a disentangled representation, a discriminative approach that is drastically different from previous approaches.
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage
This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries.
Blockchain-Based Decentralized Energy Management Platform for Residential Distributed Energy Resources in A Virtual Power Plant
In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP.
Privacy-Preserving Transactive Energy Management for IoT-aided Smart Homes via Blockchain
Further, we design an efficient blockchain system tailored for IoT devices and develop the smart contract to support the holistic transactive energy management system.
Asset Allocation via Machine Learning and Applications to Equity Portfolio Management
In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets.
Cooperative Energy Management of HVAC via Transactive Energy
However, how to scale up HVAC energy management for a group of users while persevering users' privacy remains a big challenge.
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles
To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles.
DCANet: Learning Connected Attentions for Convolutional Neural Networks
With DCANet, all attention blocks in a CNN model are trained jointly, which improves the ability of attention learning.
MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations
Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc.
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
Face anti-spoofing has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks.
Channel Pruning via Optimal Thresholding
By using OT, most negligible or unimportant channels are pruned to achieve high sparsity while minimizing performance degradation.
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement
In addition to conventional compression techniques, e. g., weight pruning and quantization, removing unimportant activations can reduce the amount of data communication and the computation cost.
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning
With the rapid scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for improving deployment efficiency.
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds.
Ranked #3 on
3D Object Detection
on OPV2V
Integral Pruning on Activations and Weights for Efficient Neural Networks
With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment.
