Search Results for author:
Found 83 papers, 23 papers with code
GroundingBooth: Grounding Text-to-Image Customization
In this work, we introduce GroundingBooth, a framework that achieves zero-shot instance-level spatial grounding on both foreground subjects and background objects in the text-to-image customization task.
AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities
This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines.
COMPOSE: Comprehensive Portrait Shadow Editing
Existing portrait relighting methods struggle with precise control over facial shadows, particularly when faced with challenges such as handling hard shadows from directional light sources or adjusting shadows while remaining in harmony with existing lighting conditions.
Flow to Rare Events: An Application of Normalizing Flow in Temporal Importance Sampling for Automated Vehicle Validation
To solve it, we devise a method to represent, generate, and reweight the distribution of risky rare events.
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians.
Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks
Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data.
SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing
Compared with existing methods for personalized subject swapping, SwapAnything has three unique advantages: (1) precise control of arbitrary objects and parts rather than the main subject, (2) more faithful preservation of context pixels, (3) better adaptation of the personalized concept to the image.
Ethos and Pathos in Online Group Discussions: Corpora for Polarisation Issues in Social Media
Growing polarisation in society caught the attention of the scientific community as well as news media, which devote special issues to this phenomenon.
MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors
To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture.
IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation
Generative object compositing emerges as a promising new avenue for compositional image editing.
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems.
Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image
At the core of portrait photography is the search for ideal lighting and viewpoint.
A Preliminary Exploration of YouTubers' Use of Generative-AI in Content Creation
Content creators increasingly utilize generative artificial intelligence (Gen-AI) on platforms such as YouTube, TikTok, Instagram, and various blogging sites to produce imaginative images, AI-generated videos, and articles using Large Language Models (LLMs).
Diffusion Model-Based Image Editing: A Survey
In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field.
VRMN-bD: A Multi-modal Natural Behavior Dataset of Immersive Human Fear Responses in VR Stand-up Interactive Games
Understanding and recognizing emotions are important and challenging issues in the metaverse era.
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
These limitations call for an effective and comprehensive solution that detects and mitigates data misuse without requiring exact training data while respecting the proprietary nature of such data.
Relightful Harmonization: Lighting-aware Portrait Background Replacement
Portrait harmonization aims to composite a subject into a new background, adjusting its lighting and color to ensure harmony with the background scene.
Perceptual Artifacts Localization for Image Synthesis Tasks
Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks.
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research.
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Knowledge from animals and humans inspires robotic innovations.
Neural Categorical Priors for Physics-Based Character Control
Although this prior distribution can be trained with the supervision of the encoder's output, it follows the original motion clip distribution in the dataset and could lead to imbalanced behaviors in our setting.
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development.
Recent Advances in Hierarchical Multi-label Text Classification: A Survey
Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical.
Multi Label Text Classification
Multi-Label Text Classification
+1
LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs
First, it takes $O(n^6)$ for two sequences where n is the average sequence length.
Neural Quantile Optimization for Edge-Cloud Networking
The network structure reflects the edge-cloud computing topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems.
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Real-world graphs generally have only one kind of tendency in their connections.
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
A Survey on Fairness-aware Recommender Systems
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
LightPainter: Interactive Portrait Relighting with Freehand Scribble
Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map.
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding
To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures.
Semi-supervised Parametric Real-world Image Harmonization
Learning-based image harmonization techniques are usually trained to undo synthetic random global transformations applied to a masked foreground in a single ground truth photo.
PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing
To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections.
Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks
In this paper, we pioneer the exploration of the interaction between the privacy risks of edge leakage and the individual fairness of a GNN.
How Far are We from Robust Long Abstractive Summarization?
For long document abstractive models, we show that the constant strive for state-of-the-art ROUGE results can lead us to generate more relevant summaries but not factual ones.
LinearCoFold and LinearCoPartition: Linear-Time Algorithms for Secondary Structure Prediction of Interacting RNA molecules
Vienna RNAcofold, which reduces the problem into the classical single sequence folding by concatenating two strands, scales in cubic time against the combined sequence length, and is slow for long sequences.
Anti-Delay Kalman Filter Fusion Algorithm for Vehicle-borne Sensor Network with Finite-Time Convergence
Intelligent vehicles in autonomous driving and obstacle avoidance, the precise relative state of vehicles put forward a higher demand.
Controllable Shadow Generation Using Pixel Height Maps
It can be used to calculate hard shadows in a 2D image based on the projective geometry, providing precise control of the shadows' direction and shape.
LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments
Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics.
A Flexible Diffusion Model
Despite the empirical success of the hand-crafted fixed forward SDEs, a great quantity of proper forward SDEs remain unexplored.
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.
Rethinking Portrait Matting with Privacy Preserving
P3M-10k consists of 10, 421 high resolution face-blurred portrait images along with high-quality alpha mattes, which enables us to systematically evaluate both trimap-free and trimap-based matting methods and obtain some useful findings about model generalization ability under the privacy preserving training (PPT) setting.
Ranked #1 on
Image Matting
on P3M-10k
Interactive Portrait Harmonization
To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected \emph{region} in the reference image instead of the entire background.
Projective Ranking-based GNN Evasion Attacks
By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes.
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation
Deep image matting methods have achieved increasingly better results on benchmarks (e. g., Composition-1k/alphamatting. com).
Temporal Knowledge Graph Completion: A Survey
KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
Lite Vision Transformer with Enhanced Self-Attention
We propose Lite Vision Transformer (LVT), a novel light-weight transformer network with two enhanced self-attention mechanisms to improve the model performances for mobile deployment.
HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars
To handle complicated motions (e. g., self-occlusions), we then leverage the encoded information on the UV manifold to construct a 3D volumetric representation based on a dynamic pose-conditioned neural radiance field.
Co-evolution Transformer for Protein Contact Prediction
These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reflection of collective covariation patterns of numerous homologous proteins.
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.
Fine Grained Human Evaluation for English-to-Chinese Machine Translation: A Case Study on Scientific Text
Recent research suggests that neural machine translation (MT) in the news domain has reached human-level performance, but for other professional domains, it is far below the level.
SSH: A Self-Supervised Framework for Image Harmonization
Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background images.
Federated Learning Meets Natural Language Processing: A Survey
Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering
We introduce DoubleField, a novel framework combining the merits of both surface field and radiance field for high-fidelity human reconstruction and rendering.
Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions
We find that sufficient conditions for such a linear dependence result are through learning algorithms that produce a uniformly Lipschitz and consistent estimator in the hypothesis space that retains certain characteristics of the drift coefficients, such as the usual linear growth condition that guarantees the existence of solutions of the underlying SDEs.
A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN
In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision.
Mask Guided Matting via Progressive Refinement Network
We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance.
A Backward SDE Method for Uncertainty Quantification in Deep Learning
We develop a probabilistic machine learning method, which formulates a class of stochastic neural networks by a stochastic optimal control problem.
Deep Image Compositing
In this paper, we propose a new method which can automatically generate high-quality image compositing without any user input.
Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?
To improve the applicability and generalizability of research results, we analyzed what ingredients in a study would facilitate an understanding of why a ML/DL technique was selected for a specific SE problem.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.
Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism
In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle.
Algorithm for Optimized mRNA Design Improves Stability and Immunogenicity
Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine.
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Based on the ELBOs, we propose a VAE-based Bayesian MF framework.
FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing
With the proposed Fusion-discriminator which takes frequency information as additional priors, our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts.
Dual affine moment invariants
In this paper, we propose a general framework to derive moment invariants under DAT for objects in M-dimensional space with N channels, which can be called dual-affine moment invariants (DAMI).
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction
Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems.
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation.
Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis
A pre-trained VGG-Face network is used to extract the attributes from the visible image.
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks
Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures.
Differential and integral invariants under Mobius transformation
According to the Liouville Theorem, an important part of the conformal transformation is the Mobius transformation, so we focus on Mobius transformation and propose two differential expressions that are invariable under 2-D and 3-D Mobius transformation respectively.
Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results
In this work, we identify the next set of challenges that requires attention from the research community and collect a new dataset of face images that involve these issues such as weather-based degradations, motion blur, focus blur and several others.
Densely Connected Pyramid Dehazing Network
We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together.
Ranked #6 on
Image Dehazing
on RESIDE-6K
Density-aware Single Image De-raining using a Multi-stream Dense Network
In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method.
Ranked #6 on
Single Image Deraining
on RainCityscapes
Fast and Efficient Calculations of Structural Invariants of Chirality
And the five chiral invariants have four characteristics:(1) They play an important role in the detection of symmetry, especially in the treatment of 'false zero' problem.
Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces
Previous approaches utilize a two-step procedure (visible feature estimation and visible image reconstruction) to synthesize the visible image given the corresponding polarimetric thermal image.
Joint Transmission Map Estimation and Dehazing using Deep Networks
Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature.
SAR Image Despeckling Using a Convolutional Neural Network
Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle.
Sparse Representation-based Open Set Recognition
We propose a generalized Sparse Representation- based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training.
Image De-raining Using a Conditional Generative Adversarial Network
Hence, it is important to solve the problem of single image de-raining/de-snowing.
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
[Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.
