Search Results for author:
Found 88 papers, 26 papers with code
ConsisLoRA: Enhancing Content and Style Consistency for LoRA-based Style Transfer
In this paper, we comprehensively analyze the limitations of the standard diffusion parameterization, which learns to predict noise, in the context of style transfer.
Multi-View Depth Consistent Image Generation Using Generative AI Models: Application on Architectural Design of University Buildings
To solve this issue, we propose a novel three-stage consistent image generation framework using generative AI models to generate architectural designs from shoebox model representations.
Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research.
Efficiently Integrate Large Language Models with Visual Perception: A Survey from the Training Paradigm Perspective
However, with the advent of Large Language Models (LLMs), there has been a notable shift towards incorporating LLMs with vision modalities.
Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities
The increasing size and complexity of pre-trained language models have demonstrated superior performance in many applications, but they usually require large training datasets to be adequately trained.
STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction
The most challenging task, aspect sentiment quad prediction (ASQP), predicts these elements simultaneously, hindered by difficulties in accurately coupling different sentiment elements.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model
We also present a new design drawing dataset ProLines for evaluation.
Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning
Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words.
Sketch-Guided Motion Diffusion for Stylized Cinemagraph Synthesis
In this paper, we propose Sketch2Cinemagraph, a sketch-guided framework that enables the conditional generation of stylized cinemagraphs from freehand sketches.
MLLA-UNet: Mamba-like Linear Attention in an Efficient U-Shape Model for Medical Image Segmentation
Our contributions include the novel 2D segmentation architecture and its empirical validation.
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis
Specifically, we explore three data augmentation strategies based on ChatGPT: context-focused, aspect-focused, and context-aspect data augmentation techniques.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+7
YOLOO: You Only Learn from Others Once
YOLOO empowers the point cloud encoder to learn a unified tri-modal representation (UTR) from point clouds and other modalities, such as images and textual cues, all at once.
Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
Topic taxonomy discovery aims at uncovering topics of different abstraction levels and constructing hierarchical relations between them.
Sketch-Guided Scene Image Generation
In this study, we propose a novel sketch-guided scene image generation framework, decomposing the task of scene image scene generation from sketch inputs into object-level cross-domain generation and scene-level image construction.
RVISA: Reasoning and Verification for Implicit Sentiment Analysis
To identify implicit sentiment with reliable reasoning, this study proposes RVISA, a two-stage reasoning framework that harnesses the generation ability of DO LLMs and the reasoning ability of ED LLMs to train an enhanced reasoner.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
Lost in UNet: Improving Infrared Small Target Detection by Underappreciated Local Features
UNet and its variants, as popular detection backbone networks, downsample the local features early and cause the irreversible loss of these local features, leading to both the missed and false detection of small targets in infrared images.
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
With the popularity of social media, people have turned to online forums, such as Reddit to express their feelings and seek support.
TexControl: Sketch-Based Two-Stage Fashion Image Generation Using Diffusion Model
In this work, we propose TexControl, a sketch-based fashion generation framework that uses a two-stage pipeline to generate the fashion image corresponding to the sketch input.
DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks
This work shows that increasing the diversity of a training dataset can improve classification model performance.
FilterPrompt: A Simple yet Efficient Approach to Guide Image Appearance Transfer in Diffusion Models
Achieving this requires the effective decoupling of key attributes within the input image data to achieve representations accurately.
RLGNet: Repeating-Local-Global History Network for Temporal Knowledge Graph Reasoning
To address this issue, we propose a multi-scale hybrid architecture model based on ensemble learning, called RLGNet (Repeating-Local-Global History Network).
Generative AI Models for Different Steps in Architectural Design: A Literature Review
Although architects recognize the potential of generative AI in design, personal barriers often restrict their access to the latest technological developments, thereby causing the application of generative AI in architectural design to lag behind.
ECNet: Effective Controllable Text-to-Image Diffusion Models
Secondly, to overcome the issue of limited conditional supervision, we introduce Diffusion Consistency Loss (DCL), which applies supervision on the denoised latent code at any given time step.
FriendNet: Detection-Friendly Dehazing Network
In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions?
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities.
Cross Initialization for Face Personalization of Text-to-Image Models
Additionally a fast version of our method allows for capturing an input image in roughly 26 seconds while surpassing the baseline methods in terms of both reconstruction and editability.
Cross Initialization for Personalized Text-to-Image Generation
Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability.
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
The demands for fine-tuning PLMs, especially LLMs, have led to a surge in the development of PEFT methods, as depicted in Fig.
Cross-BERT for Point Cloud Pretraining
By performing cross-modal interaction, Cross-BERT can smoothly reconstruct the masked tokens during pretraining, leading to notable performance enhancements for downstream tasks.
MicroGlam: Microscopic Skin Image Dataset with Cosmetics
We repeated the process for the same skin patch under three cosmetic products.
Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions
To highlight the context in such special causal relationships, we propose a new task to determine whether or not an input pair of emotion and cause has a valid causal relationship under different contexts and extract the specific context clauses that participate in the causal relationship.
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Label Supervised LLaMA Finetuning
We evaluate this approach with Label Supervised LLaMA (LS-LLaMA), based on LLaMA-2-7B, a relatively small-scale LLM, and can be finetuned on a single GeForce RTX4090 GPU.
Ranked #1 on
Named Entity Recognition (NER)
on CoNLL03
(F1 (micro) metric)
Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Effective patient monitoring is vital for timely interventions and improved healthcare outcomes.
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
The deep learning models achieved state-of-the-art results in both prediction and classification tasks.
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
The PDRL framework is able to learn the future states of the traffic and weather forecasting and the cumulative rewards are gradually increasing over each episode.
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
These issues compromise both the accuracy and the computational efficiency of models in both Machine Learning and Unlearning.
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL).
TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective
We investigate the reasons for this phenomenon and discover that the existing data augmentation approach and hard sample mining strategy are incompatible with ViTs-based FR backbone due to the lack of tailored consideration on preserving face structural information and leveraging each local token information.
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare.
AniFaceDrawing: Anime Portrait Exploration during Your Sketching
In the second stage, we simulated the drawing process of the generated images without any additional data (labels) and trained the sketch encoder for incomplete progressive sketches to generate high-quality portrait images with feature alignment to the disentangled representations in the teacher encoder.
Recurrent Attention Networks for Long-text Modeling
Revisiting the self-attention mechanism and the recurrent structure, this paper proposes a novel long-document encoding model, Recurrent Attention Network (RAN), to enable the recurrent operation of self-attention.
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models.
Search By Image: Deeply Exploring Beneficial Features for Beauty Product Retrieval
Searching by image is popular yet still challenging due to the extensive interference arose from i) data variations (e. g., background, pose, visual angle, brightness) of real-world captured images and ii) similar images in the query dataset.
Sketch2Cloth: Sketch-based 3D Garment Generation with Unsigned Distance Fields
In this study, we propose Sketch2Cloth, a sketch-based 3D garment generation system using the unsigned distance fields from the user's sketch input.
DiffFaceSketch: High-Fidelity Face Image Synthesis with Sketch-Guided Latent Diffusion Model
We then introduce a Stochastic Region Abstraction (SRA), an approach to augment our dataset to improve the robustness of SGLDM to handle sketch input with arbitrary abstraction.
Sentiment analysis and opinion mining on educational data: A survey
In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically.
AI enabled RPM for Mental Health Facility
To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously.
SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing
We propose an interactive editing method that allows humans to help deep neural networks (DNNs) learn a latent space more consistent with human knowledge, thereby improving classification accuracy on indistinguishable ambiguous data.
Rethinking Generative Methods for Image Restoration in Physics-based Vision: A Theoretical Analysis from the Perspective of Information
In this study, we try to re-interpret these generative methods for image restoration tasks using information theory.
SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
However, it is difficult to draw a proper scene graph for image retrieval, image generation, and multi-modal applications.
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection
We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds.
PointSee: Image Enhances Point Cloud
There is a trend to fuse multi-modal information for 3D object detection (3OD).
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information.
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios.
Contrastive Semantic-Guided Image Smoothing Network
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD).
PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection
Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas.
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion.
Ranked #2 on
Point Cloud Completion
on ShapeNet-ViPC
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.
Dynamic Message Propagation Network for RGB-D Salient Object Detection
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects.
Efficient Human-in-the-loop System for Guiding DNNs Attention
Attention guidance is an approach to addressing dataset bias in deep learning, where the model relies on incorrect features to make decisions.
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
To our knowledge, RSOD is the first quantitatively evaluated and graded snowy OD dataset.
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.
When A Conventional Filter Meets Deep Learning: Basis Composition Learning on Image Filters
Image filters are fast, lightweight and effective, which make these conventional wisdoms preferable as basic tools in vision tasks.
Interactive 3D Character Modeling from 2D Orthogonal Drawings with Annotations
We propose an interactive 3D character modeling approach from orthographic drawings (e. g., front and side views) based on 2D-space annotations.
Stroke Correspondence by Labeling Closed Areas
First, the proposed system separates the closed areas in each keyframe and estimates the correspondences between closed areas by using the characteristics of shape, depth, and closed area connection.
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference
Topic modeling has been widely used for discovering the latent semantic structure of documents, but most existing methods learn topics with a flat structure.
Learning Perceptual Manifold of Fonts
To solve the specific issue, we propose the perceptual manifold of fonts to visualize the perceptual adjustment in the latent space of a generative model of fonts.
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
Image Deformation Estimation via Multi-Objective Optimization
Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs).
dualFace:Two-Stage Drawing Guidance for Freehand Portrait Sketching
In the stage of local guidance, we synthesize detailed portrait images with a deep generative model from user-drawn contour lines, but use the synthesized results as detailed drawing guidance.
Sketch-based Normal Map Generation with Geometric Sampling
This paper proposes a deep generative model for generating normal maps from users sketch with geometric sampling.
Context Reinforced Neural Topic Modeling over Short Texts
As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abilities.
Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling
Topic modeling has been one of the most active research areas in machine learning in recent years.
Neural Mixed Counting Models for Dispersed Topic Discovery
Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics.
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.
Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification
The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e. g., hand-craft features and domain expert knowledge).
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
We have validated our approach on four recognized datasets (three synthetic and one real-world).
Siamese Network-Based Supervised Topic Modeling
Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification.
On the Effectiveness of Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator.
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images.
A Network Framework for Noisy Label Aggregation in Social Media
This paper focuses on the task of noisy label aggregation in social media, where users with different social or culture backgrounds may annotate invalid or malicious tags for documents.
Cultural Vocal Bursts Intensity Prediction
Image Classification
+2
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.
