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Found 37 papers, 10 papers with code
Accelerating Vision-Language Pretraining with Free Language Modeling
FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted.
Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos
TEG learns to adaptively ground the possible event proposals given a set of sentences by estimating the cross-modal distance in a joint semantic space.
Evolutionary Reinforcement Learning: A Survey
This article presents a comprehensive survey of state-of-the-art methods for integrating EC into RL, referred to as evolutionary reinforcement learning (EvoRL).
EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation
Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices.
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games
In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: i) both the sampling efficiency and training speed are doubled when running PPO on Google football game; ii) the training speed is 13 times faster when running PBT+PPO on Pong game.
Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation
The main challenges of applying NAS to semantic segmentation arise from two aspects: (i) high-resolution images to be processed; (ii) additional requirement of real-time inference speed (i. e., real-time semantic segmentation) for applications such as autonomous driving.
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.
Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures
First, we formulate the NAS problem for enhancing adversarial robustness of deep neural networks into a multiobjective optimization problem.
Exploiting Context Information for Generic Event Boundary Captioning
Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information.
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
Existing vision-language pre-training (VLP) methods primarily rely on paired image-text datasets, which are either annotated by enormous human labors, or crawled from the internet followed by elaborate data cleaning techniques.
Spotlights: Probing Shapes from Spherical Viewpoints
Recent years have witnessed the surge of learned representations that directly build upon point clouds.
S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization
In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization.
Optically-generated focused ultrasound for noninvasive brain stimulation with ultrahigh precision
Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision.
Semantic-Aware Pretraining for Dense Video Captioning
This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021.
R4: A Framework for Route Representation and Route Recommendation
For user representation, we utilize a series of historical navigation to extract user preference.
Accelerating Multi-Objective Neural Architecture Search by Random-Weight Evaluation
For the goal of automated design of high-performance deep convolutional neural networks (CNNs), Neural Architecture Search (NAS) methodology is becoming increasingly important for both academia and industries. Due to the costly stochastic gradient descent (SGD) training of CNNs for performance evaluation, most existing NAS methods are computationally expensive for real-world deployments.
Revisiting Self-Training for Few-Shot Learning of Language Model
In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM.
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information.
End-to-End Dense Video Captioning with Parallel Decoding
Dense video captioning aims to generate multiple associated captions with their temporal locations from the video.
Ranked #2 on
Dense Video Captioning
on YouCook2
FaPN: Feature-aligned Pyramid Network for Dense Image Prediction
Recent advancements in deep neural networks have made remarkable leap-forwards in dense image prediction.
Ranked #20 on
Semantic Segmentation
on ADE20K val
Photoacoustic Silk Scaffolds for Neural stimulation and Regeneration
We also confirmed that photoacoustic neural stimulation promoted neurite outgrowth by impacting the brain-derived neurotrophic factor (BDNF) pathway.
Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutions
Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings.
S3Net: 3D LiDAR Sparse Semantic Segmentation Network
In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches.
Spin Nernst Effect of Antiferromagnetic Magnons in the Presence of Spin Diffusion
Magnon spin Nernst effect was recently proposed as an intrinsic effect in antiferromagnets, where spin diffusion and boundary spin transmission have been ignored.
Mesoscale and Nanoscale Physics Materials Science Other Condensed Matter
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.
Ranked #1 on
3D Semantic Segmentation
on nuScenes
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike.
Ranked #1 on
3D Semantic Scene Completion
on SemanticKITTI
Multi-objective Neural Architecture Search with Almost No Training
In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries.
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.
SoloGAN: Multi-domain Multimodal Unpaired Image-to-Image Translation via a Single Generative Adversarial Network
Existing I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only.
Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction
With an exponential explosive growth of various digital text information, it is challenging to efficiently obtain specific knowledge from massive unstructured text information.
Ternary Compression for Communication-Efficient Federated Learning
Learning over massive data stored in different locations is essential in many real-world applications.
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search
Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)
The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks
In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods.
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks
Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i. e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
Multiobjective Test Problems with Degenerate Pareto Fronts
The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i. e., the dimension of the Pareto front of the $m$-objective problem be less than (m-1).
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization
To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multi-objective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators.
