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Shiwei Zhang, Jiayu Wang, Yingya Zhang +6 authors
The cascaded I2VGen-XL approach uses hierarchical encoders and text-image pairs to improve the semantic accuracy, continuity, and clarity of generated videos.
Jiahao Li, Hao Tan, Kai Zhang +7 authors
Instant3D rapidly generates high-quality 3D assets from text using a two-stage approach involving a fine-tuned 2D diffusion model and a transformer-based reconstructor.
In Gim, Guojun Chen, Seung-seob Lee +3 authors
Prompt Cache improves inference speed for large language models by reusing precomputed attention states of common text segments in user prompts.
Di Chang, Yichun Shi, Quankai Gao +6 authors
MagicDance, a diffusion-based model, transfers human motion and facial expression across identities in dance videos, achieving high generalization and robust appearance control.
Antoine Guédon, Vincent Lepetit
A method for fast and precise mesh extraction from 3D Gaussian Splatting using a regularization term and Poisson reconstruction, enabling real-time editing and rendering quality.
Sang-Hoon Lee, Ha-Yeong Choi, Seung-Bin Kim +1 authors
HierSpeech++, an enhanced hierarchical variational autoencoder, achieves high-quality zero-shot speech synthesis by combining self-supervised representations and an efficient super-resolution framework.
Katherine Tian, Eric Mitchell, Huaxiu Yao +2 authors
Fine-tuning language models using automatically generated factuality preference rankings improves their factual accuracy without human labeling.
Da Yin, Faeze Brahman, Abhilasha Ravichander +4 authors
Lumos, a modular language agent framework using open-source LLMs, achieves superior performance across various tasks and demonstrates enhanced generalization.
Ying Sheng, Shiyi Cao, Dacheng Li +9 authors
S-LoRA is a system that allows for efficient and scalable serving of numerous LoRA adapters using a unified memory pool, tensor parallelism, and custom CUDA kernels.
Risto Luukkonen, Ville Komulainen, Jouni Luoma +18 authors
The study addresses the challenges of creating large language models for underrepresented languages like Finnish, developing both monolingual and multilingual models, and evaluating their performance through a newly created benchmark.
Yufei Wang, Zhou Xian, Feng Chen +6 authors
RoboGen automates the learning of diverse robotic skills at scale using generative simulation to propose tasks and generate training environments and supervision.
Clifton Poth, Hannah Sterz, Indraneil Paul +7 authors
Adapters is an open-source library designed for parameter-efficient and modular transfer learning in large language models, offering a unified interface for various adapter methods.
Zhongfei Qing, Zhongang Cai, Zhitao Yang +1 authors
A new system combines language models and motion retrieval to generate long, stable, and semantically aligned human motions from text descriptions, outperforming previous methods in trajectory, action, and blending tasks.
Shengnan An, Zexiong Ma, Zeqi Lin +3 authors
LeMa, a learning-from-mistakes approach, enhances LLMs' mathematical reasoning by learning from inaccurate reasoning paths corrected by GPT-4, surpassing SOTA performance on math problems.
Kai Yang, Jian Tao, Jiafei Lyu +6 authors
D3PO method directly fine-tunes diffusion models using human feedback without requiring a separate reward model, reducing computational cost and improving image quality.
Bin Lin, Bin Zhu, Yang Ye +3 authors
Video-LLaVA is a unified large vision-language model that enhances performance across various image and video benchmarks by integrating visual representations into the language feature space.
Haoning Wu, Zicheng Zhang, Erli Zhang +11 authors
The Q-Instruct dataset, enhanced with diverse human feedback, improves low-level visual perception and understanding in multi-modality foundation models.
Weihan Wang, Qingsong Lv, Wenmeng Yu +13 authors
CogVLM, a visual language foundation model, uses a trainable visual expert module to deeply integrate vision and language without compromising NLP performance.
Animesh Sinha, Bo Sun, Anmol Kalia +14 authors
Style Tailoring is a finetuning method for Latent Diffusion Models that enhances the generation of sticker images, improving visual quality, prompt alignment, and scene diversity.
Qiying Yu, Quan Sun, Xiaosong Zhang +4 authors
CapsFusion is an advanced framework that improves multimodal pretraining data by combining web-based image-text pairs and synthetic captions, leading to enhanced model performance, sample efficiency, and scalability.
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