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Found 37 papers, 17 papers with code
Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality
Thus motivated, we study heterogeneous decision making by AVs and HVs in a mixed traffic environment, aiming to capture the interactions between human and machine decision-making and develop an AI foundation that enables vehicles to operate safely and efficiently.
AdaWM: Adaptive World Model based Planning for Autonomous Driving
World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning policy.
Towards Unraveling and Improving Generalization in World Models
World models have recently emerged as a promising approach to reinforcement learning (RL), achieving state-of-the-art performance across a wide range of visual control tasks.
Speech-Forensics: Towards Comprehensive Synthetic Speech Dataset Establishment and Analysis
Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation.
Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO
This paper Proposes a novel cervical cancer dual-stained image recognition (DSIR-YOLO) model based on an YOLOv5.
MSSIDD: A Benchmark for Multi-Sensor Denoising
Furthermore, we propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features, thereby facilitating the generalization of the consistent model to unseen sensors.
CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving
2) Built-in tasks: CarDreamer offers a comprehensive set of highly configurable driving tasks which are compatible with Gym interfaces and are equipped with empirically optimized reward functions.
IVAC-P2L: Leveraging Irregular Repetition Priors for Improving Video Action Counting
Our research addresses the shortfall by introducing a novel approach to VAC, called Irregular Video Action Counting (IVAC).
Activating Wider Areas in Image Super-Resolution
The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR).
CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
A variety of defenses have been proposed against Trojans planted in (backdoor attacks on) deep neural network (DNN) classifiers.
Meta-Adapter: An Online Few-shot Learner for Vision-Language Model
Without bells and whistles, our approach outperforms the state-of-the-art online few-shot learning method by an average of 3. 6\% on eight image classification datasets with higher inference speed.
Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge.
Post-Training Overfitting Mitigation in DNN Classifiers
Well-known (non-malicious) sources of overfitting in deep neural net (DNN) classifiers include: i) large class imbalances; ii) insufficient training-set diversity; and iii) over-training.
Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection
Deep neural networks are vulnerable to backdoor attacks (Trojans), where an attacker poisons the training set with backdoor triggers so that the neural network learns to classify test-time triggers to the attacker's designated target class.
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
To this end, the primary objective of this work is to build a fundamental understanding on ``\textit{whether and when online learning can be significantly accelerated by a warm-start policy from offline RL?}''.
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
It is known that the estimation bias hinges heavily on the ensemble size (i. e., the number of Q-function approximators used in the target), and that determining the `right' ensemble size is highly nontrivial, because of the time-varying nature of the function approximation errors during the learning process.
U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
In this paper, we construct a user needs-centric E-commerce conversational recommendation dataset (U-NEED) from real-world E-commerce scenarios.
Omni Aggregation Networks for Lightweight Image Super-Resolution
While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions.
Deep Arbitrary-Scale Image Super-Resolution via Scale-Equivariance Pursuit
The ability of scale-equivariance processing blocks plays a central role in arbitrary-scale image super-resolution tasks.
Object-fabrication Targeted Attack for Object Detection
Evaluation results demonstrate that, the proposed object-fabrication targeted attack mode and the corresponding targeted feature space attack method show significant improvements in terms of image-specific attack, universal performance and generalization capability, compared with the previous targeted attack for object detection.
Learning Continuous Depth Representation via Geometric Spatial Aggregator
Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision.
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic
Our detector leverages the influence of the backdoor attack, independent of the backdoor embedding mechanism, on the landscape of the classifier's outputs prior to the softmax layer.
Cross-category Video Highlight Detection via Set-based Learning
For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework.
Self-supervised Graph-level Representation Learning with Local and Global Structure
This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery.
X-volution: On the unification of convolution and self-attention
Convolution and self-attention are acting as two fundamental building blocks in deep neural networks, where the former extracts local image features in a linear way while the latter non-locally encodes high-order contextual relationships.
Ranked #84 on
Instance Segmentation
on COCO minival
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks
Deep Neural Networks (DNNs) have been shown vulnerable to Test-Time Evasion attacks (TTEs, or adversarial examples), which, by making small changes to the input, alter the DNN's decision.
3D Human Action Representation Learning via Cross-View Consistency Pursuit
In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary supervision signal.
Graphical Modeling for Multi-Source Domain Adaptation
Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem compared to the conventional single-source domain adaptation.
Shape Self-Correction for Unsupervised Point Cloud Understanding
Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal.
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement
We propose to disentangle graph structure and node attributes into two distinct sets of representations, and such disentanglement can be done in either the input or the embedding space.
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
Specifically, we assume that agents maintain local estimates of the global state based on their local information and communications with neighbors.
System Identification via Meta-Learning in Linear Time-Varying Environments
System identification is a fundamental problem in reinforcement learning, control theory and signal processing, and the non-asymptotic analysis of the corresponding sample complexity is challenging and elusive, even for linear time-varying (LTV) systems.
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation.
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
Cross-domain Detection via Graph-induced Prototype Alignment
To mitigate these problems, we propose a Graph-induced Prototype Alignment (GPA) framework to seek for category-level domain alignment via elaborate prototype representations.
Wasserstein-Bounded Generative Adversarial Networks
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
Bi-stream Pose Guided Region Ensemble Network for Fingertip Localization from Stereo Images
To mitigate these limitations and promote further research on hand pose estimation from stereo images, we propose a new large-scale binocular hand pose dataset called THU-Bi-Hand, offering a new perspective for fingertip localization.
Motion feature augmented network for dynamic hand gesture recognition from skeletal data
Dynamic hand gesture recognition has attracted increasing attention because of its importance for human–computer interaction.
