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Found 9 papers, 4 papers with code
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation
Revolutionary advancements in text-to-image models have unlocked new dimensions for sophisticated content creation, e. g., text-conditioned image editing, allowing us to edit the diverse images that convey highly complex visual concepts according to the textual guidance.
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Recent advancements indicate that scaling up Multimodal Large Language Models (MLLMs) effectively enhances performance on downstream multimodal tasks.
Ranked #72 on
Visual Question Answering
on MM-Vet
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain).
ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation
The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General Intelligence (AGI).
Learning to Reweight for Graph Neural Network
In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings.
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer
Active Domain Adaptation (ADA) aims to maximally boost model adaptation in a new target domain by actively selecting a limited number of target data to annotate. This setting neglects the more practical scenario where training data are collected from multiple sources.
IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
The aforementioned two trends enable the device-cloud collaborative and dynamic recommendation, which deeply exploits the recommendation pattern among cloud-device data and efficiently characterizes different instances with different underlying distributions based on the cost of frequent device-cloud communication.
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud.
Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling
In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model.
