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Found 28 papers, 10 papers with code
Faceptor: A Generalist Model for Face Perception
This design enhances the unification of model structure while improving application efficiency in terms of storage overhead.
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
We find that FER models remember noisy samples by focusing on a part of the features that can be considered related to the noisy labels instead of learning from the whole features that lead to the latent truth.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
In recent years, facial expression recognition (FER) has garnered significant attention within the realm of computer vision research.
Ranked #1 on
Facial Expression Recognition (FER)
on AffectNet
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Relative Uncertainty Learning for Facial Expression Recognition
To quantify these uncertainties and achieve good performance under noisy data, we regard uncertainty as a relative concept and propose an innovative uncertainty learning method called Relative Uncertainty Learning (RUL).
Ranked #10 on
Facial Expression Recognition (FER)
on RAF-DB
Facial Expression Recognition
Facial Expression Recognition (FER)
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
We also discuss notable benchmarks commonly utilized in the field.
LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm
Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately.
Ranked #15 on
Audio Classification
on ICBHI Respiratory Sound Database
Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks
In this work, we consider the problem of detecting a variety of socially abnormal driving behaviors, i. e., behaviors that do not conform to the behavior of other nearby drivers.
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples.
Model and Data Agreement for Learning with Noisy Labels
We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective.
Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation
Based on these observations, a calibration-based dual prototypical contrastive learning (CDPCL) approach is proposed to reduce the domain discrepancy between the learned class-wise features and the prototypes of different domains for domain generalization semantic segmentation.
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
The location of broken insulators in aerial images is a challenging task.
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-level autonomous driving.
Semi-supervised Contrastive Learning with Similarity Co-calibration
Semi-supervised learning acts as an effective way to leverage massive unlabeled data.
DUAL ADVERSARIAL MODEL FOR GENERATING 3D POINT CLOUD
Three-dimensional data, such as point clouds, are often composed of three coordinates with few featrues.
Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images
It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images.
One-Bit Active Query With Contrastive Pairs
The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling.
Observer-Based Coordinated Tracking Control for Nonlinear Multi-Agent Systems with Intermittent Communication under Heterogeneous Coupling Framework
In this article, the observer-based coordinated tracking control problem for a class of nonlinear multi-agent systems(MASs) with intermittent communication and information constraints is studied under dynamic switching topology.
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation
In this paper, we propose a class-wise non-salient region generalized (CNSG) framework for the VGSS task.
Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective
We further provide interesting analyses of the effects of backbones and IND/OOD datasets on OOD detection performance.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
CAV Traffic Control to Mitigate the Impact of Congestion from Bottlenecks: A Linear Quadratic Regulator Approach and Microsimulation Study
The effectiveness of the proposed traffic control algorithms is tested using a traffic control example and compared with existing proportional-integral (PI)- and model predictive control (MPC)- based controllers from the literature.
Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption
Motivated by the synthetic control method, we construct a synthetic treatment group for the target population by a weighted mixture of treatment groups of source populations.
Investigating the Robustness and Properties of Detection Transformers (DETR) Toward Difficult Images
We studied this issue by measuring the performance of DETR with different experiments and benchmarking the network with convolutional neural network (CNN) based detectors like YOLO and Faster-RCNN.
MARVEL: Multi-Agent Reinforcement-Learning for Large-Scale Variable Speed Limits
Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions.
Vision-based Learning for Drones: A Survey
Drones as advanced cyber-physical systems are undergoing a transformative shift with the advent of vision-based learning, a field that is rapidly gaining prominence due to its profound impact on drone autonomy and functionality.
AdvCloak: Customized Adversarial Cloak for Privacy Protection
This strategy initially focuses on adapting the masks to the unique individual faces via image-specific training and then enhances their feature-level generalization ability to diverse facial variations of individuals via person-specific training.
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query
In DeLR, the query is based on region-level, and we only annotate the object region that is queried; 2) Instead of directly providing both localization and recognition annotations, we separately query the two components, and thus reduce the recognition budget with the pseudo class labels provided by the model.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
Open-Set Facial Expression Recognition
Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
