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Found 8 papers, 2 papers with code
Multi-LoRA Composition for Image Generation
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.
Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models
We propose Causal Context Generation, Causal-CoG, which is a prompting strategy that engages contextual information to enhance precise VQA during inference.
Adapting LLM Agents with Universal Feedback in Communication
To optimize agent interactions for task-specific learning with our universal buffer and pipeline, we introduce diverse communication patterns tailored for both single-agent and multi-agent environments.
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
We find that scaling LMM consistently enhances model performance and improves language capabilities, and performance of LoRA/QLoRA tuning of LMM are comparable to the performance of full-model fine-tuning.
Ranked #50 on
Visual Question Answering
on MM-Vet
Efficient RLHF: Reducing the Memory Usage of PPO
To address this issue, we present a comprehensive analysis the memory usage, performance, and training time of memory-savings techniques for PPO.
In-Context Learning Unlocked for Diffusion Models
We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models.
Progressive Neural Image Compression with Nested Quantization and Latent Ordering
We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream.
Differentiable Joint Pruning and Quantization for Hardware Efficiency
We present a differentiable joint pruning and quantization (DJPQ) scheme.
