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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.
Understanding Self-attention Mechanism via Dynamical System Perspective
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models.
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.
Causal Inference for Chatting Handoff
However, most existing methods mainly focus on the dialogue context or assist with global satisfaction prediction based on multi-task learning, which ignore the grounded relationships among the causal variables, like the user state and labor cost.
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.
