772 research outputs found
Adaptive Domain Generalization via Online Disagreement Minimization
Deep neural networks suffer from significant performance deterioration when
there exists distribution shift between deployment and training. Domain
Generalization (DG) aims to safely transfer a model to unseen target domains by
only relying on a set of source domains. Although various DG approaches have
been proposed, a recent study named DomainBed, reveals that most of them do not
beat the simple Empirical Risk Minimization (ERM). To this end, we propose a
general framework that is orthogonal to existing DG algorithms and could
improve their performance consistently. Unlike previous DG works that stake on
a static source model to be hopefully a universal one, our proposed AdaODM
adaptively modifies the source model at test time for different target domains.
Specifically, we create multiple domain-specific classifiers upon a shared
domain-generic feature extractor. The feature extractor and classifiers are
trained in an adversarial way, where the feature extractor embeds the input
samples into a domain-invariant space, and the multiple classifiers capture the
distinct decision boundaries that each of them relates to a specific source
domain. During testing, distribution differences between target and source
domains could be effectively measured by leveraging prediction disagreement
among source classifiers. By fine-tuning source models to minimize the
disagreement at test time, target domain features are well aligned to the
invariant feature space. We verify AdaODM on two popular DG methods, namely ERM
and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and
TerraIncognita. The results show AdaODM stably improves the generalization
capacity on unseen domains and achieves state-of-the-art performance.Comment: 11 pages, 4 figure
Adv3D: Generating 3D Adversarial Examples in Driving Scenarios with NeRF
Deep neural networks (DNNs) have been proven extremely susceptible to
adversarial examples, which raises special safety-critical concerns for
DNN-based autonomous driving stacks (i.e., 3D object detection). Although there
are extensive works on image-level attacks, most are restricted to 2D pixel
spaces, and such attacks are not always physically realistic in our 3D world.
Here we present Adv3D, the first exploration of modeling adversarial examples
as Neural Radiance Fields (NeRFs). Advances in NeRF provide photorealistic
appearances and 3D accurate generation, yielding a more realistic and
realizable adversarial example. We train our adversarial NeRF by minimizing the
surrounding objects' confidence predicted by 3D detectors on the training set.
Then we evaluate Adv3D on the unseen validation set and show that it can cause
a large performance reduction when rendering NeRF in any sampled pose. To
generate physically realizable adversarial examples, we propose primitive-aware
sampling and semantic-guided regularization that enable 3D patch attacks with
camouflage adversarial texture. Experimental results demonstrate that the
trained adversarial NeRF generalizes well to different poses, scenes, and 3D
detectors. Finally, we provide a defense method to our attacks that involves
adversarial training through data augmentation. Project page:
https://len-li.github.io/adv3d-we
Rethinking Rendering in Generalizable Neural Surface Reconstruction: A Learning-based Solution
Generalizable neural surface reconstruction techniques have attracted great
attention in recent years. However, they encounter limitations of low
confidence depth distribution and inaccurate surface reasoning due to the
oversimplified volume rendering process employed. In this paper, we present
Reconstruction TRansformer (ReTR), a novel framework that leverages the
transformer architecture to redesign the rendering process, enabling complex
photon-particle interaction modeling. It introduces a learnable meta-ray token
and utilizes the cross-attention mechanism to simulate the interaction of
photons with sampled points and render the observed color. Meanwhile, by
operating within a high-dimensional feature space rather than the color space,
ReTR mitigates sensitivity to projected colors in source views. Such
improvements result in accurate surface assessment with high confidence. We
demonstrate the effectiveness of our approach on various datasets, showcasing
how our method outperforms the current state-of-the-art approaches in terms of
reconstruction quality and generalization ability.Comment: 18 pages, 11 Figures, Our code will be released at
https://github.com/YixunLiang/ReT
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
HyperThumbnail: Real-time 6K Image Rescaling with Rate-distortion Optimization
Contemporary image rescaling aims at embedding a high-resolution (HR) image
into a low-resolution (LR) thumbnail image that contains embedded information
for HR image reconstruction. Unlike traditional image super-resolution, this
enables high-fidelity HR image restoration faithful to the original one, given
the embedded information in the LR thumbnail. However, state-of-the-art image
rescaling methods do not optimize the LR image file size for efficient sharing
and fall short of real-time performance for ultra-high-resolution (e.g., 6K)
image reconstruction. To address these two challenges, we propose a novel
framework (HyperThumbnail) for real-time 6K rate-distortion-aware image
rescaling. Our framework first embeds an HR image into a JPEG LR thumbnail by
an encoder with our proposed quantization prediction module, which minimizes
the file size of the embedding LR JPEG thumbnail while maximizing HR
reconstruction quality. Then, an efficient frequency-aware decoder reconstructs
a high-fidelity HR image from the LR one in real time. Extensive experiments
demonstrate that our framework outperforms previous image rescaling baselines
in rate-distortion performance and can perform 6K image reconstruction in real
time.Comment: Accepted by CVPR 2023; Github Repository:
https://github.com/AbnerVictor/HyperThumbnai
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