Search Results for author:
Found 34 papers, 10 papers with code
Facial Affective Behavior Analysis with Instruction Tuning
Our initiative on the dataset and benchmarks reveal the nature and rationale of facial affective behaviors, i. e., fine-grained facial movement, interpretability, and reasoning.
Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-labeling
However, previous 3D scene graph generation methods utilize a fully supervised learning manner and require a large amount of entity-level annotation data of objects and relations, which is extremely resource-consuming and tedious to obtain.
Images are Achilles' Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models
In this paper, we study the harmlessness alignment problem of multimodal large language models (MLLMs).
The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative
Unlike direct harmful output generation for MLLMs, our research demonstrates how a single MLLM agent can be subtly influenced to generate prompts that, in turn, induce other MLLM agents in the society to output malicious content.
Repositioning the Subject within Image
Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task.
Temporal Adaptive RGBT Tracking with Modality Prompt
RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving.
Ranked #2 on
Rgb-T Tracking
on RGBT210
CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when the data is scarce or imbalanced.
A Novel Tree Model-based DNN to Achieve a High-Resolution DOA Estimation via Massive MIMO receive array
To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity.
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model
The target instruction is then forwarded to a visual grounding system for object pose and size estimation, following which the robot grasps the object accordingly.
A New Heterogeneous Hybrid Massive MIMO Receiver with An Intrinsic Ability of Removing Phase Ambiguity of DOA Estimation via Machine Learning
To address this problem, a new heterogeneous sub-connected hybrid analog and digital (HAD) MIMO structure is proposed with an intrinsic ability of removing phase ambiguity and a corresponding new framework is developed to implement a rapid high-precision DOA estimation using only single time-slot.
MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis
The prevalent DA approaches in MIA encompass conventional DA, synthetic DA, and automatic DA.
Evaluating Object Hallucination in Large Vision-Language Models
Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i. e. they tend to generate objects that are inconsistent with the target images in the descriptions.
Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation
To solve it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM) into NAR text-to-text generation and integrates BART to improve the performance.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Diffusion Models for Non-autoregressive Text Generation: A Survey
In this survey, we review the recent progress in diffusion models for NAR text generation.
Low-Complexity Three-Dimensional AOA-Cross Geometric Center Localization Methods via Multi-UAV network
It is not able to estimate angle noise but has lower computational complexity.
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and Correction
Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
A 2030 United States Macro Grid Unlocking Geographical Diversity to Accomplish Clean Energy Goals
Some U. S. states have set clean energy goals and targets in an effort to decarbonize their electricity sectors.
Deep Learning Based DOA Estimation for Hybrid Massive MIMO Receive Array with Overlapped Subarrays
To improve the accuracy of direction-of-arrival (DOA) estimation, a deep learning (DL)-based method called CDAE-DNN is proposed for hybrid analog and digital (HAD) massive MIMO receive array with overlapped subarray (OSA) architecture in this paper.
Recent Progress in Transformer-based Medical Image Analysis
The mainstream classification and segmentation tasks are further divided into eleven medical image modalities.
Optimal Measurement of Drone Swarm in RSS-based Passive Localization with Region Constraints
In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated.
Affective Behaviour Analysis Using Pretrained Model with Facial Priori
As a result, we utilize AffectNet pretrained CNN to extract expression scores concatenating with expression and AU scores from ViT to obtain the final VA features.
LQoCo: Learning to Optimize Cache Capacity Overloading in Storage Systems
Cache plays an important role to maintain high and stable performance (i. e. high throughput, low tail latency and throughput jitter) in storage systems.
Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array
The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70\% with massive MIMO receive array.
Identifying and mitigating bias in algorithms used to manage patients in a pandemic
Numerous COVID-19 clinical decision support systems have been developed.
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
In this paper, we propose an adaptive unsupervised learning approach for efficient MRI intra-tumor partitioning and glioblastoma survival prediction.
High-performance Passive Eigen-model-based Detectors of Single Emitter Using Massive MIMO Receivers
The test statistic (TS) of the first method is defined as the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) while that of the second one is defined as the ratio of Max-EV to noise variance (R-MaxEV-NV).
$\rm ^{83}Rb$/$\rm ^{83m}Kr$ production and cross-section measurement with 3.4 MeV and 20 MeV proton beams
$\rm ^{83m}Kr$, with a short lifetime, is an ideal calibration source for liquid xenon or liquid argon detectors.
Nuclear Experiment Instrumentation and Detectors
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes.
Enhanced RSS-based UAV Localization via Trajectory and Multi-base Stations
First, fixing the nth BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule.
RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification
We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification.
GUIDEGAN: ATTENTION BASED SPATIAL GUIDANCE FOR IMAGE-TO-IMAGE TRANSLATION
To tackle this problem, we propose a GuideGAN based on attention mechanism.
Generative Adversarial Network
Image-to-Image Translation
+1
Identification of Pleonastic It Using the Web
In a significant minority of cases, certain pronouns, especially the pronoun it, can be used without referring to any specific entity.
