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Found 34 papers, 13 papers with code
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction
Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment.
S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields
Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images.
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction
Therefore, how to select effective pseudo labels to denoise DS data is still a challenge in document-level distant relation extraction.
AutoColor: Learned Light Power Control for Multi-Color Holograms
For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information.
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU
Recent years have witnessed impressive progress in super-resolution (SR) processing.
Underwater target detection based on improved YOLOv7
Underwater target detection is a crucial aspect of ocean exploration.
Recurrent Contour-based Instance Segmentation with Progressive Learning
In this work, we propose a novel deep network architecture, i. e., PolySnake, for contour-based instance segmentation.
Ranked #1 on
Semantic Contour Prediction
on Sbd val
Dirichlet-Neumann learning algorithm for solving elliptic interface problems
Non-overlapping domain decomposition methods are natural for solving interface problems arising from various disciplines, however, the numerical simulation requires technical analysis and is often available only with the use of high-quality grids, thereby impeding their use in more complicated situations.
Force-Aware Interface via Electromyography for Natural VR/AR Interaction
Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users.
A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems
The goal of building intelligent dialogue systems has largely been separately pursued under two motives: task-oriented dialogue (TOD) systems, and open-domain systems for chit-chat (CC).
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #15 on
Domain Generalization
on PACS
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks
Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradient to update model parameters, which becomes time-consuming as the network goes deeper.
Layer-Parallel Training of Residual Networks with Auxiliary Variables
Backpropagation algorithm is indispensable for training modern residual networks (ResNets) and usually tends to be time-consuming due to its inherent algorithmic lockings.
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.
FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality
To tackle these problems toward six-degrees-of-freedom, egocentric, and stereo NeRF in VR, we present the first gaze-contingent 3D neural representation and view synthesis method.
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.
Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm
In this paper, we propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC).
Recurrent Model Predictive Control
This paper proposes an off-line algorithm, called Recurrent Model Predictive Control (RMPC), to solve general nonlinear finite-horizon optimal control problems.
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-level autonomous driving.
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning
Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge.
DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation
Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems.
A Practical Layer-Parallel Training Algorithm for Residual Networks
Gradient-based algorithms for training ResNets typically require a forward pass of the input data, followed by back-propagating the objective gradient to update parameters, which are time-consuming for deep ResNets.
Deep Multi Depth Panoramas for View Synthesis
We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs.
Mixed Reinforcement Learning with Additive Stochastic Uncertainty
This paper presents a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy with the purpose of improving both learning accuracy and training speed.
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic
In this paper, we introduce the minimax formulation and distributional framework to improve the generalization ability of RL algorithms and develop the Minimax Distributional Soft Actor-Critic (Minimax DSAC) algorithm.
Direct and indirect reinforcement learning
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints
CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem.
DiffTaichi: Differentiable Programming for Physical Simulation
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities.
Stochastic Training of Residual Networks: a Differential Equation Viewpoint
During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models.
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
Nonlocal neural networks have been proposed and shown to be effective in several computer vision tasks, where the nonlocal operations can directly capture long-range dependencies in the feature space.
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment
In this document we present an end-to-end machine reading comprehension system that solves multiple choice questions with a textual entailment perspective.
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
