791 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
High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation
Due to limited camera capacities, digital images usually have a narrower
dynamic illumination range than real-world scene radiance. To resolve this
problem, High Dynamic Range (HDR) reconstruction is proposed to recover the
dynamic range to better represent real-world scenes. However, due to different
physical imaging parameters, the tone-mapping functions between images and real
radiance are highly diverse, which makes HDR reconstruction extremely
challenging. Existing solutions can not explicitly clarify a corresponding
relationship between the tone-mapping function and the generated HDR image, but
this relationship is vital when guiding the reconstruction of HDR images. To
address this problem, we propose a method to explicitly estimate the tone
mapping function and its corresponding HDR image in one network. Firstly, based
on the characteristics of the tone mapping function, we construct a model by a
polynomial to describe the trend of the tone curve. To fit this curve, we use a
learnable network to estimate the coefficients of the polynomial. This curve
will be automatically adjusted according to the tone space of the Low Dynamic
Range (LDR) image, and reconstruct the real HDR image. Besides, since all
current datasets do not provide the corresponding relationship between the tone
mapping function and the LDR image, we construct a new dataset with both
synthetic and real images. Extensive experiments show that our method
generalizes well under different tone-mapping functions and achieves SOTA
performance
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