Search Results for author:
Found 10 papers, 7 papers with code
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
This strategy can implicitly enhance the model's robustness during the optimization process, mitigating instability risks arising from multimodal information inputs.
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study.
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i. e., the ability to recognize and focus on key pixel regions in an image.
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts.
ASR: Attention-alike Structural Re-parameterization
This technique enables the mitigation of the extra costs for performance improvement during training, such as parameter size and inference time, through these transformations during inference, and therefore SRP has great potential for industrial and practical applications.
Deepening Neural Networks Implicitly and Locally via Recurrent Attention Strategy
More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings.
Switchable Self-attention Module
Attention mechanism has gained great success in vision recognition.
Mix-Pooling Strategy for Attention Mechanism
Recently many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision.
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
