2,237 research outputs found
Convergence of the Ginzburg-Landau approximation for the Ericksen-Leslie system
We establish the local well-posedness of the general Ericksen-Leslie system
in liquid crystals with the initial velocity and director field in . In particular, we prove that the solutions of the Ginzburg-Landau
approximation system converge smoothly to the solution of the Ericksen-Leslie
system for any with a maximal existence time of the
Ericksen- Leslie system
COCA: Classifier-Oriented Calibration for Source-Free Universal Domain Adaptation via Textual Prototype
Universal Domain Adaptation (UniDA) aims to distinguish common and private
classes between the source and target domains where domain shift exists.
Recently, due to more stringent data restrictions, researchers have introduced
Source-Free UniDA (SF-UniDA) in more realistic scenarios. SF-UniDA methods
eliminate the need for direct access to source samples when performing
adaptation to the target domain. However, existing SF-UniDA methods still
require an extensive quantity of labeled source samples to train a source
model, resulting in significant labeling costs. To tackle this issue, we
present a novel Classifier-Oriented Calibration (COCA) method. This method,
which leverages textual prototypes, is formulated for the source model based on
few-shot learning. Specifically, we propose studying few-shot learning, usually
explored for closed-set scenarios, to identify common and domain-private
classes despite a significant domain shift between source and target domains.
Essentially, we present a novel paradigm based on the vision-language model to
learn SF-UniDA and hugely reduce the labeling costs on the source domain.
Experimental results demonstrate that our approach outperforms state-of-the-art
UniDA and SF-UniDA models
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
Benefiting from prompt tuning, recent years have witnessed the promising
performance of pre-trained vision-language models, e.g., CLIP, on versatile
downstream tasks. In this paper, we focus on a particular setting of learning
adaptive prompts on the fly for each test sample from an unseen new domain,
which is known as test-time prompt tuning (TPT). Existing TPT methods typically
rely on data augmentation and confidence selection. However, conventional data
augmentation techniques, e.g., random resized crops, suffers from the lack of
data diversity, while entropy-based confidence selection alone is not
sufficient to guarantee prediction fidelity. To address these issues, we
propose a novel TPT method, named DiffTPT, which leverages pre-trained
diffusion models to generate diverse and informative new data. Specifically, we
incorporate augmented data by both conventional method and pre-trained stable
diffusion to exploit their respective merits, improving the models ability to
adapt to unknown new test data. Moreover, to ensure the prediction fidelity of
generated data, we introduce a cosine similarity-based filtration technique to
select the generated data with higher similarity to the single test sample. Our
experiments on test datasets with distribution shifts and unseen categories
demonstrate that DiffTPT improves the zero-shot accuracy by an average of
5.13\% compared to the state-of-the-art TPT method. Our code and models will be
publicly released.Comment: Proceedings of the IEEE/CVF International Conference on Computer
Vision 202
Online Knowledge Distillation with Diverse Peers
Distillation is an effective knowledge-transfer technique that uses predicted
distributions of a powerful teacher model as soft targets to train a
less-parameterized student model. A pre-trained high capacity teacher, however,
is not always available. Recently proposed online variants use the aggregated
intermediate predictions of multiple student models as targets to train each
student model. Although group-derived targets give a good recipe for
teacher-free distillation, group members are homogenized quickly with simple
aggregation functions, leading to early saturated solutions. In this work, we
propose Online Knowledge Distillation with Diverse peers (OKDDip), which
performs two-level distillation during training with multiple auxiliary peers
and one group leader. In the first-level distillation, each auxiliary peer
holds an individual set of aggregation weights generated with an
attention-based mechanism to derive its own targets from predictions of other
auxiliary peers. Learning from distinct target distributions helps to boost
peer diversity for effectiveness of group-based distillation. The second-level
distillation is performed to transfer the knowledge in the ensemble of
auxiliary peers further to the group leader, i.e., the model used for
inference. Experimental results show that the proposed framework consistently
gives better performance than state-of-the-art approaches without sacrificing
training or inference complexity, demonstrating the effectiveness of the
proposed two-level distillation framework.Comment: Accepted to AAAI-202
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction
Magnetic resonance (MR) image acquisition is an inherently prolonged process,
whose acceleration by obtaining multiple undersampled images simultaneously
through parallel imaging has always been the subject of research. In this
paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable
of learning multi-scale spatial-frequency features from both real and imaginary
components, for fast parallel MR image reconstruction. By reformulating the
complex operations using octave convolutions, our model shows a strong ability
to capture richer representations of MR images, while at the same time greatly
reducing the spatial redundancy. More specifically, the input feature maps and
convolutional kernels are first split into two components (i.e., real and
imaginary), which are then divided into four groups according to their spatial
frequencies. Then, our Dual-OctConv conducts intra-group information updating
and inter-group information exchange to aggregate the contextual information
across different groups. Our framework provides two appealing benefits: (i) it
encourages interactions between real and imaginary components at various
spatial frequencies to achieve richer representational capacity, and (ii) it
enlarges the receptive field by learning multiple spatial-frequency features of
both the real and imaginary components. We evaluate the performance of the
proposed model on the acceleration of multi-coil MR image reconstruction.
Extensive experiments are conducted on an {in vivo} knee dataset under
different undersampling patterns and acceleration factors. The experimental
results demonstrate the superiority of our model in accelerated parallel MR
image reconstruction. Our code is available at:
github.com/chunmeifeng/Dual-OctConv.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence
(AAAI) 202
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