From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring

A novel approach to enhancing seismic foundation models has been introduced, synergizing multigrid algorithms with vision transformers. This method addresses the unique characteristics of seismic data, which require specialized processing techniques. The proposed adaptive two-grid foundation model training strategy (ADATG) utilizes Hilbert encoding to effectively capture both high- and low-frequency features in seismogram data, improving the efficiency of seismic data analysis and model training.

Recent research highlights the potential of task arithmetic for editing pre-trained closed-vocabulary models, particularly in image classification. This study investigates task addition in closed-vocabulary models, revealing that weight disentanglement is a common outcome of pre-training. The findings suggest that closed-vocabulary vision transformers can be effectively modified using task arithmetic, leading to enhanced multi-task model deployment capabilities.

Artificial intelligence (AI) in media has seen rapid advancements over the past decade, particularly with the introduction of Generative Adversarial Networks (GANs) and diffusion models, which have enhanced photorealistic image generation. However, these developments have also led to challenges in distinguishing between real and synthetic content, as evidenced by the rise of deepfakes. Many detection models utilizing deep learning methods like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been created, but they often struggle with generalization and multimodal data.

The paper titled 'LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers' presents a new method for quantizing pre-trained Vision Transformer models. The proposed Layer-wise Mixed Precision Quantization (LampQ) addresses limitations in existing quantization methods, such as coarse granularity and metric scale mismatches. By employing a type-aware Fisher-based metric, LampQ aims to enhance both the efficiency and accuracy of quantization in various tasks, including image classification and object detection.