40 research outputs found
Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
Continual semantic segmentation aims to learn new classes while maintaining
the information from the previous classes. Although prior studies have shown
impressive progress in recent years, the fairness concern in the continual
semantic segmentation needs to be better addressed. Meanwhile, fairness is one
of the most vital factors in deploying the deep learning model, especially in
human-related or safety applications. In this paper, we present a novel
Fairness Continual Learning approach to the semantic segmentation problem. In
particular, under the fairness objective, a new fairness continual learning
framework is proposed based on class distributions. Then, a novel Prototypical
Contrastive Clustering loss is proposed to address the significant challenges
in continual learning, i.e., catastrophic forgetting and background shift. Our
proposed loss has also been proven as a novel, generalized learning paradigm of
knowledge distillation commonly used in continual learning. Moreover, the
proposed Conditional Structural Consistency loss further regularized the
structural constraint of the predicted segmentation. Our proposed approach has
achieved State-of-the-Art performance on three standard scene understanding
benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC, and promoted the fairness
of the segmentation model
FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding
Although Domain Adaptation in Semantic Scene Segmentation has shown
impressive improvement in recent years, the fairness concerns in the domain
adaptation have yet to be well defined and addressed. In addition, fairness is
one of the most critical aspects when deploying the segmentation models into
human-related real-world applications, e.g., autonomous driving, as any unfair
predictions could influence human safety. In this paper, we propose a novel
Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation. In
particular, from the proposed formulated fairness objective, a new adaptation
framework will be introduced based on the fair treatment of class
distributions. Moreover, to generally model the context of structural
dependency, a new conditional structural constraint is introduced to impose the
consistency of predicted segmentation. Thanks to the proposed Conditional
Structure Network, the self-attention mechanism has sufficiently modeled the
structural information of segmentation. Through the ablation studies, the
proposed method has shown the performance improvement of the segmentation
models and promoted fairness in the model predictions. The experimental results
on the two standard benchmarks, i.e., SYNTHIA Cityscapes and GTA5
Cityscapes, have shown that our method achieved State-of-the-Art (SOTA)
performance.Comment: Accepted to CVPR'2
Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
In this work, we investigate the problem of face reconstruction given a
facial feature representation extracted from a blackbox face recognition
engine. Indeed, it is a very challenging problem in practice due to the
limitations of abstracted information from the engine. We, therefore, introduce
a new method named Attention-based Bijective Generative Adversarial Networks in
a Distillation framework (DAB-GAN) to synthesize the faces of a subject given
his/her extracted face recognition features. Given any unconstrained unseen
facial features of a subject, the DAB-GAN can reconstruct his/her facial images
in high definition. The DAB-GAN method includes a novel attention-based
generative structure with the newly defined Bijective Metrics Learning
approach. The framework starts by introducing a bijective metric so that the
distance measurement and metric learning process can be directly adopted in the
image domain for an image reconstruction task. The information from the
blackbox face recognition engine will be optimally exploited using the global
distillation process. Then an attention-based generator is presented for a
highly robust generator to synthesize realistic faces with ID preservation. We
have evaluated our method on the challenging face recognition databases, i.e.,
CelebA, LFW, CFP-FP, CP-LFW, AgeDB, CA-LFW, and consistently achieved
state-of-the-art results. The advancement of DAB-GAN is also proven in both
image realism and ID preservation properties.Comment: arXiv admin note: substantial text overlap with arXiv:2003.0695
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models
Recognition across domains has recently become an active topic in the
research community. However, it has been largely overlooked in the problem of
recognition in new unseen domains. Under this condition, the delivered deep
network models are unable to be updated, adapted or fine-tuned. Therefore,
recent deep learning techniques, such as: domain adaptation, feature
transferring, and fine-tuning, cannot be applied. This paper presents a novel
Universal Non-volume Preserving approach to the problem of domain
generalization in the context of deep learning. The proposed method can be
easily incorporated with any other ConvNet framework within an end-to-end deep
network design to improve the performance. On digit recognition, we benchmark
on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and
MNIST-M. The proposed method is also experimented on face recognition on
Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the
state-of-the-art methods. In the problem of pedestrian detection, we
empirically observe that the proposed method learns models that improve
performance across a priori unknown data distributions
A Multitask Data-Driven Model for Battery Remaining Useful Life Prediction
Lithium-ion batteries (LIBs) have recently been used widely in moving devices. Understand status of the batteries can help to predict the failure and improve the effectiveness of using them. There are some lithium-ion information that define the battery health over time. These are state-of-charge (SOC), state-of-health (SOH), and remaining-useful-life (RUL). Normally, a LIB is working under charging and discharging cycles continuously. In this paper, we will focus on the data dependency of different time-slots in a cycle and in a sequence of cycles to retrieve RUL. We leverage multi-channel inputs such as temperature, voltage, current and the nature of peaks cross the cycles to improve our prediction. Comparing to existing methods, the experiments show that we can improve from 0.040 to 0.033 (reduce 17.5%) in RMSE loss, which is significant
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer
vision problem due to its emerging applicability in several real-world
applications. Despite a large number of existing works, solving the data
association problem in any MC-MOT pipeline is arguably one of the most
challenging tasks. Developing a robust MC-MOT system, however, is still highly
challenging due to many practical issues such as inconsistent lighting
conditions, varying object movement patterns, or the trajectory occlusions of
the objects between the cameras. To address these problems, this work,
therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP)
approach to solve the data association task. Compared to existing methods, our
new model offers several advantages, including better feature representations
and the ability to recover from lost tracks during camera transitions.
Moreover, our model works gracefully regardless of the overlapping ratios
between the cameras. Experimental results show that we outperform existing
MC-MOT algorithms by a large margin on several practical datasets. Notably, our
model works favorably on online settings but can be extended to an incremental
approach for large-scale datasets.Comment: accepted at CVPR 202