Search Results for author:
Found 60 papers, 35 papers with code
SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model
Synonym and antonym practice are the most common practices in our early childhood.
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans.
Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes
Finally, we introduce user-specific customization, where we model the unique characteristics of the target face identity and perform semantic reasoning via MLLM to enable personalized and explainable deepfake detection.
Veracity Bias and Beyond: Uncovering LLMs' Hidden Beliefs in Problem-Solving Reasoning
Despite LLMs' explicit alignment against demographic stereotypes, they have been shown to exhibit biases under various social contexts.
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios.
A Survey on Large Language Model based Human-Agent Systems
Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Exploring Temporal Dynamics in Event-based Eye Tracker
Eye-tracking is a vital technology for human-computer interaction, especially in wearable devices such as AR, VR, and XR.
VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models
In this work, we integrate Multimodal Large Language Models (MLLMs) within DM-based face forensics, and propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators.
TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data Consistency
Test-time computing approaches, which leverage additional computational resources during inference, have been proven effective in enhancing large language model performance.
Mixup Model Merge: Enhancing Model Merging Performance through Randomized Linear Interpolation
In conclusion, M$^3$ is a simple yet effective model merging method that significantly enhances the performance of the merged model by randomly generating contribution ratios for two fine-tuned LLMs.
ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring
Motion deblurring addresses the challenge of image blur caused by camera or scene movement.
PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection
Based on the views, a scale augmentation module and an angle acquisition module are constructed.
Frequency-aware Event Cloud Network
Event cameras are biologically inspired sensors that emit events asynchronously with remarkable temporal resolution, garnering significant attention from both industry and academia.
Event-based Motion Deblurring via Multi-Temporal Granularity Fusion
Effectively utilizing the high-temporal-resolution event data is crucial for extracting precise motion information and enhancing deblurring performance.
Representation Purification for End-to-End Speech Translation
Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language.
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective
This paper studies the performance of large language models (LLMs), particularly regarding demographic fairness, in solving real-world healthcare tasks.
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual Grounding
Remote sensing (RS) visual grounding aims to use natural language expression to locate specific objects (in the form of the bounding box or segmentation mask) in RS images, enhancing human interaction with intelligent RS interpretation systems.
Text4Seg: Reimagining Image Segmentation as Text Generation
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge.
PRF: Parallel Resonate and Fire Neuron for Long Sequence Learning in Spiking Neural Networks
The PRF enables efficient long sequence learning while maintaining parallel training.
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges
However, these approaches are often validated with traditional benchmarks and early-stage LLMs, leaving uncertainty about their effectiveness when evaluating state-of-the-art LLMs on the contamination of more challenging benchmarks.
PersonaGym: Evaluating Persona Agents and LLMs
Persona agents, which are LLM agents conditioned to act according to an assigned persona, enable contextually rich and user aligned interactions across domains like education and healthcare.
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak Attacks
Specifically, we query the model to generate a fallacious yet deceptively real procedure for the harmful behavior.
Towards Vision-Language Geo-Foundation Model: A Survey
Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding.
A3:Ambiguous Aberrations Captured via Astray-Learning for Facial Forgery Semantic Sublimation
The primary objective of the proposed method is to blend hybrid forgery semantics derived from high-frequency components into authentic imagery, named aberrations.
Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba
In contrast, Point Cloud is a popular representation for processing 3-dimensional data and serves as an alternative method to exploit local and global spatial features.
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction.
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models.
Modeling Low-Resource Health Coaching Dialogues via Neuro-Symbolic Goal Summarization and Text-Units-Text Generation
Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues.
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy.
A Simple and Effective Point-based Network for Event Camera 6-DOFs Pose Relocalization
These cameras implicitly capture movement and depth information in events, making them appealing sensors for Camera Pose Relocalization (CPR) tasks.
CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models.
A Survey on Data Augmentation in Large Model Era
Leveraging large models, these data augmentation techniques have outperformed traditional approaches.
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems.
DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank
During crisis events, people often use social media platforms such as Twitter to disseminate information about the situation, warnings, advice, and support.
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations.
SpikePoint: An Efficient Point-based Spiking Neural Network for Event Cameras Action Recognition
Moreover, it also achieves SOTA performance across all methods on three datasets, utilizing approximately 0. 3\% of the parameters and 0. 5\% of power consumption employed by artificial neural networks (ANNs).
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
Traffic sign detection is an important research direction in intelligent driving.
Ranked #1 on
Traffic Sign Detection
on CCTSDB2021
Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences.
TTPOINT: A Tensorized Point Cloud Network for Lightweight Action Recognition with Event Cameras
In the experiment, TTPOINT emerged as the SOTA method on three datasets while also attaining SOTA among point cloud methods on all five datasets.
DefCor-Net: Physics-Aware Ultrasound Deformation Correction
The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis.
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection.
Ranked #1 on
Object Detection In Aerial Images
on DOTA 1.0
X-PuDu at SemEval-2022 Task 7: A Replaced Token Detection Task Pre-trained Model with Pattern-aware Ensembling for Identifying Plausible Clarifications
This paper describes our winning system on SemEval 2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts.
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection
Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection.
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects.
Network Pruning via Feature Shift Minimization
In this paper, we propose a novel Feature Shift Minimization (FSM) method to compress CNN models, which evaluates the feature shift by converging the information of both features and filters.
MMRotate: A Rotated Object Detection Benchmark using PyTorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning.
The KFIoU Loss for Rotated Object Detection
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
Multi-Robot Collaborative Perception with Graph Neural Networks
Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents.
Network Compression via Central Filter
In this paper, by exploring the similarities between feature maps, we propose a novel filter pruning method, Central Filter (CF), which suggests that a filter is approximately equal to a set of other filters after appropriate adjustments.
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
AlphaRotate is an open-source Tensorflow benchmark for performing scalable rotation detection on various datasets.
Generalized Nesterov's Acceleration-incorporated Non-negative and Adaptive Latent Factor Analysis
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service applications.
Impact of Local Energy Markets on the Distribution Systems: A Comprehensive Review
In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources.
A large family of maximum scattered linear sets of $\mathrm{PG}(1,q^n)$ and their associated MRD codes
In this paper, we provide a large family of new maximum scattered linear sets over $\mathrm{PG}(1, q^n)$ for any even $n\geq 6$ and odd $q$.
Combinatorics 94B05, 11T06, 15A04
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc.
Ranked #35 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
AIPerf: Automated machine learning as an AI-HPC benchmark
The de facto HPC benchmark LINPACK can not reflect AI computing power and I/O performance without representative workload.
Stock Index Prediction with Multi-task Learning and Word Polarity Over Time
We adopt BERT with multitask learning which additionally predicts the worthiness of the news and propose a metric called Polarity-Over-Time to extract the word polarity among different event periods.
Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images
Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.
Using Machine Learning to Forecast Future Earnings
In this essay, we have comprehensively evaluated the feasibility and suitability of adopting the Machine Learning Models on the forecast of corporation fundamentals (i. e. the earnings), where the prediction results of our method have been thoroughly compared with both analysts' consensus estimation and traditional statistical models.
Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets
Moreover, the vagueness of satire and news parody determines that a news tweet can hardly be classified with a binary decision, that is, satirical or legitimate.
