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Found 10 papers, 2 papers with code
Keeping Representation Similarity in Finetuning for Medical Image Analysis
Foundation models pretrained on large-scale natural images have been widely used to adapt to medical image analysis through finetuning.
A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies, significantly influencing the advancement trajectory of image processing and computer vision.
Towards Unifying Understanding and Generation in the Era of Vision Foundation Models: A Survey from the Autoregression Perspective
First, we present the trend for next generation of vision foundation models, i. e., unifying both understanding and generation in vision tasks.
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images
In this work, we facilitate the study of the performance of PEFT when adapting foundation models to medical image classification tasks.
FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection
In this work, we propose FM-OV3D, a method of Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection, which improves the open-vocabulary localization and recognition abilities of 3D model by blending knowledge from multiple pre-trained foundation models, achieving true open-vocabulary without facing constraints from original 3D datasets.
Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights
In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks.
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
However, most backdoor attacks have to modify the neural network models through training with poisoned data and/or direct model editing, which leads to a common but false belief that backdoor attack can be easily avoided by properly protecting the model.
Convergence of Two-Layer Regression with Nonlinear Units
Softmax unit and ReLU unit are the key structure in attention computation.
In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick
Given matrices $A_1 \in \mathbb{R}^{n \times d}$, and $A_2 \in \mathbb{R}^{n \times d}$ and $B \in \mathbb{R}^{n \times n}$, the purpose is to solve some certain optimization problems: Normalized version $\min_{X} \| D(X)^{-1} \exp(A_1 X A_2^\top) - B \|_F^2$ and Rescaled version $\| \exp(A_1 X A_2^\top) - D(X) \cdot B \|_F^2$.
WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal
To this end, we propose a method called Weather-aware Multi-scale MoE (WM-MoE) based on Transformer for blind weather removal.
