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Found 35 papers, 2 papers with code
GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation
6D object pose estimation is crucial for robotic perception and precise manipulation.
Phys4DGen: A Physics-Driven Framework for Controllable and Efficient 4D Content Generation from a Single Image
The task of 4D content generation involves creating dynamic 3D models that evolve over time in response to specific input conditions, such as images.
NexusSplats: Efficient 3D Gaussian Splatting in the Wild
While 3D Gaussian Splatting (3DGS) has recently demonstrated remarkable rendering quality and efficiency in 3D scene reconstruction, it struggles with varying lighting conditions and incidental occlusions in real-world scenarios.
An Efficient Dynamic Resource Allocation Framework for Evolutionary Bilevel Optimization
Additionally, a cooperation mechanism is integrated within the competitive framework to further enhance efficiency and prevent premature convergence.
Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization
In recent years, despite significant advancements in adversarial attack research, the security challenges in cross-modal scenarios, such as the transferability of adversarial attacks between infrared, thermal, and RGB images, have been overlooked.
Phy124: Fast Physics-Driven 4D Content Generation from a Single Image
Secondly, the extensive sampling process and the large number of parameters in diffusion models result in exceedingly time-consuming generation processes.
Adversarial Learning for Neural PDE Solvers with Sparse Data
Neural network solvers for partial differential equations (PDEs) have made significant progress, yet they continue to face challenges related to data scarcity and model robustness.
Ask, Attend, Attack: A Effective Decision-Based Black-Box Targeted Attack for Image-to-Text Models
\textit{Attack} uses an evolutionary algorithm to attack the crucial regions, where the attacks are semantically related to the target texts of \textit{Ask}, thus achieving targeted attacks without semantic loss.
Beyond Augmentation: Empowering Model Robustness under Extreme Capture Environments
This method improves the model's generalization under extreme conditions and enables learning diverse features, thus better addressing the challenges in re-ID.
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking
In the contemporary of deep learning, where models often grapple with the challenge of simultaneously achieving robustness against adversarial attacks and strong generalization capabilities, this study introduces an innovative Local Feature Masking (LFM) strategy aimed at fortifying the performance of Convolutional Neural Networks (CNNs) on both fronts.
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift
Studying adversarial attacks on artificial intelligence (AI) systems helps discover model shortcomings, enabling the construction of a more robust system.
Federated Transfer Learning Aided Interference Classification in GNSS Signals
This study delves into the classification of interference signals to global navigation satellite systems (GNSS) stemming from mobile jammers such as unmanned aerial vehicles (UAVs) across diverse wireless communication zones, employing federated learning (FL) and transfer learning (TL).
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples.
Exploring Color Invariance through Image-Level Ensemble Learning
This issue is particularly pronounced in complex wide-area surveillance scenarios, such as person re-identification and industrial dust segmentation, where models often experience a decline in performance due to overfitting on color information during training, given the presence of environmental variations.
Cross-Modality Perturbation Synergy Attack for Person Re-identification
For instance, infrared images are typically grayscale, unlike visible images that contain color information.
Pre-Evolved Model for Complex Multi-objective Optimization Problems
However, existing multi-objective evolutionary algorithms (MOEAs) encounter significant challenges in generating high-quality populations when solving diverse complex MOPs.
Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search
In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems.
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach
To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations.
An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection
Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer.
Balancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism
However, these algorithms ignore the significance of tackling this issue from the perspective of decision variables, which makes the algorithm lack the ability to search from different dimensions and limits the performance of the algorithm.
A Cooperation-Aware Lane Change Method for Autonomous Vehicles
Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration.
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model
At the same time, due to the adoption of an individual-based evolution mechanism, the computational cost of the proposed method is independent of the number of decision variables, thus avoiding the problem of exponential growth of the search space.
An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA.
Search for axion-like dark matter with spin-based amplifiers
Here, we demonstrate a new quantum sensor to search for ALPs in the mass range that spans about two decades from 8. 3 feV to 744 feV.
High Energy Physics - Phenomenology Atomic Physics Quantum Physics
Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
However, traditional evolutionary algorithms for solving LSMOPs have some deficiencies in dealing with this structural manifold, resulting in poor diversity, local optima, and inefficient searches.
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains
Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments.
Online Bagging for Anytime Transfer Learning
In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially.
Solving dynamic multi-objective optimization problems via support vector machine
The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM.
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs.
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments.
On visual BMI analysis from facial images
Various facial representations, including geometry based representations and deep learning based, are comprehensively evaluated and analyzed from three perspectives: the overall performance on visual BMI prediction, the redundancy in facial representations and the sensitivity to head pose changes.
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Face detection and recognition benchmarks have shifted toward more difficult environments.
Transfer Learning based Dynamic Multiobjective Optimization Algorithms
This approach takes the transfer learning method as a tool to help reuse the past experience for speeding up the evolutionary process, and at the same time, any population based multiobjective algorithms can benefit from this integration without any extensive modifications.
