Shared and private information learning in multimodal sentiment analysis with deep modal alignment and self-supervised multi-task learning
Designing an effective representation learning method for multimodal sentiment analysis
tasks is a crucial research direction. The challenge lies in learning both shared and private …
tasks is a crucial research direction. The challenge lies in learning both shared and private …
Lr-fpn: Enhancing remote sensing object detection with location refined feature pyramid network
Remote sensing target detection aims to identify and locate critical targets within remote
sensing images, finding extensive applications in agriculture and urban planning. Feature …
sensing images, finding extensive applications in agriculture and urban planning. Feature …
Text Guided Image Editing with Automatic Concept Locating and Forgetting
With the advancement of image-to-image diffusion models guided by text, significant
progress has been made in image editing. However, a persistent challenge remains in …
progress has been made in image editing. However, a persistent challenge remains in …
Multi-hop question answering under temporal knowledge editing
Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant
attention in the era of large language models. However, existing models for MQA under KE …
attention in the era of large language models. However, existing models for MQA under KE …
Dialectical alignment: Resolving the tension of 3h and security threats of llms
With the rise of large language models (LLMs), ensuring they embody the principles of being
helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While …
helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While …
Prompt-saw: Leveraging relation-aware graphs for textual prompt compression
Large language models (LLMs) have shown exceptional abilities for multiple different
natural language processing tasks. While prompting is a crucial tool for LLM inference, we …
natural language processing tasks. While prompting is a crucial tool for LLM inference, we …
Editable Concept Bottleneck Models
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to
elucidate the prediction process through a human-understandable concept layer. However …
elucidate the prediction process through a human-understandable concept layer. However …
Improving Concept Alignment in Vision-Language Concept Bottleneck Models
Concept Bottleneck Models (CBM) map the input image to a high-level human-
understandable concept space and then make class predictions based on these concepts …
understandable concept space and then make class predictions based on these concepts …
Semi-supervised Concept Bottleneck Models
L Hu, T Huang, H Xie, C Ren, Z Hu, L Yu… - arXiv preprint arXiv …, 2024 - arxiv.org
Concept Bottleneck Models (CBMs) have garnered increasing attention due to their ability to
provide concept-based explanations for black-box deep learning models while achieving …
provide concept-based explanations for black-box deep learning models while achieving …
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in
Large Language Models (LLMs). While best-performing solutions in this domain use a plan …
Large Language Models (LLMs). While best-performing solutions in this domain use a plan …