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Found 11 papers, 4 papers with code
Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation
Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii)the target semantics calibration module that calibrates the unreliable semantics.
Delving into the Continuous Domain Adaptation
Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e. g., art, real, painting, quickdraw, etc.
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing
For multimodal tasks, a good feature extraction network should extract information as much as possible and ensure that the extracted feature embedding and other modal feature embedding have an excellent mutual understanding.
Taylor saves for later: disentanglement for video prediction using Taylor representation
We propose a two-branch seq-to-seq deep model to disentangle the Taylor feature and the residual feature in video frames by a novel recurrent prediction module (TaylorCell) and residual module.
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography.
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference.
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
The generated reflectance, which is assumed to be irrelevant of illumination by Retinex, is treated as enhanced brightness.
Domain Generalization via Optimal Transport with Metric Similarity Learning
Previous domain generalization approaches mainly focused on learning invariant features and stacking the learned features from each source domain to generalize to a new target domain while ignoring the label information, which will lead to indistinguishable features with an ambiguous classification boundary.
Split to Be Slim: An Overlooked Redundancy in Vanilla Convolution
Therefore, instead of directly removing uncertain redundant features, we propose a \textbf{sp}lit based \textbf{conv}olutional operation, namely SPConv, to tolerate features with similar patterns but require less computation.
Pyramid Real Image Denoising Network
Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features.
Ranked #2 on
Color Image Denoising
on Darmstadt Noise Dataset
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR).
