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Found 11 papers, 4 papers with code
Higher Layers Need More LoRA Experts
In this paper, we introduce a novel parameter-efficient MoE method, \textit{\textbf{M}oE-L\textbf{o}RA with \textbf{L}ayer-wise Expert \textbf{A}llocation (MoLA)} for Transformer-based models, where each model layer has the flexibility to employ a varying number of LoRA experts.
Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
Large Vision-Language Models (LVLMs) offer remarkable benefits for a variety of vision-language tasks.
A Fast Minimization Algorithm for the Euler Elastica Model Based on a Bilinear Decomposition
A host of numerical experiments are conducted to show that the new algorithm produces good results with much-improved efficiency compared to other state-of-the-art algorithms for the EE model.
Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond
The success of data mixing augmentations in image classification tasks has been well-received.
Correlation Alignment for Unsupervised Domain Adaptation
In contrast to subspace manifold methods, it aligns the original feature distributions of the source and target domains, rather than the bases of lower-dimensional subspaces.
Ranked #8 on
Domain Adaptation
on Office-Caltech
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
CORAL is a "frustratingly easy" unsupervised domain adaptation method that aligns the second-order statistics of the source and target distributions with a linear transformation.
Ranked #4 on
Domain Generalization
on NICO Animal
Return of Frustratingly Easy Domain Adaptation
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions.
Ranked #4 on
Domain Adaptation
on Synth Digits-to-SVHN
What Do Deep CNNs Learn About Objects?
Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters.
Learning Deep Object Detectors from 3D Models
Crowdsourced 3D CAD models are becoming easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that augmenting the training data of contemporary Deep Convolutional Neural Net (DCNN) models with such synthetic data can be effective, especially when real training data is limited or not well matched to the target domain.
Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images
To produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range.
