119 citations · 252 across the 12 of their papers we have counts for
12 papers
Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers
Peng Gao, Le Zhuo, Dongyang Liu +17
Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lac…
TerDiT: Ternary Diffusion Models with Transformers
Xudong Lu, Aojun Zhou, Ziyi Lin +7
Recent developments in large-scale pre-trained text-to-image diffusion models have significantly improved the generation of high-fidelity images, particularly with the emergence of…
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
Dongyang Liu, Renrui Zhang, Longtian Qiu +16
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX…
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
Ziyi Lin, Chris Liu, Renrui Zhang +13
We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-languag…
MChat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation
Xiaowei Chi, Junbo Qi, Rongyu Zhang +3
While current LLM chatbots like GPT-4V bridge the gap between human instructions and visual representations to enable text-image generations, they still lack efficient alignment me…
Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models
Junting Pan, Ziyi Lin, Yuying Ge +5
Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs). The key idea is to convert the visual information into t…
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Peng Gao, Jiaming Han, Renrui Zhang +9
How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains…
Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking
Peng Gao, Renrui Zhang, Rongyao Fang +4
Masked Autoencoders (MAE) have been popular paradigms for large-scale vision representation pre-training. However, MAE solely reconstructs the low-level RGB signals after the decod…
Frozen CLIP Models are Efficient Video Learners
Ziyi Lin, Shijie Geng, Renrui Zhang +6
Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conductin…
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
Junting Pan, Ziyi Lin, Xiatian Zhu +2
Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard…
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
Siyu Chen, Junting Pan, Guanglu Song +6
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020. Our entry is mainly b…
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Lu Sheng, Ziyi Lin, Jing Shao +1
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images. However, the trade-off between the generalizat…