35 citations · 97 across the 33 of their papers we have counts for
17 papers · 1 filter
Divot: Diffusion Powers Video Tokenizer for Comprehension and Generation
Yuying Ge, Yizhuo Li, Yixiao Ge +1
In recent years, there has been a significant surge of interest in unifying image comprehension and generation within Large Language Models (LLMs). This growing interest has prompt…
EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios
Lu Qiu, Yi Chen, Yuying Ge +3
The advent of Multimodal Large Language Models, leveraging the power of Large Language Models, has recently demonstrated superior multimodal understanding and reasoning abilities,…
DiCoDe: Diffusion-Compressed Deep Tokens for Autoregressive Video Generation with Language Models
Yizhuo Li, Yuying Ge, Yixiao Ge +2
Videos are inherently temporal sequences by their very nature. In this work, we explore the potential of modeling videos in a chronological and scalable manner with autoregressive…
Scalable Image Tokenization with Index Backpropagation Quantization
Fengyuan Shi, Zhuoyan Luo, Yixiao Ge +3
Existing vector quantization (VQ) methods struggle with scalability, largely attributed to the instability of the codebook that undergoes partial updates during training. The codeb…
PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
Shangkun Sun, Ruyang Liu, Haoran Tang +5
In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long videos through extremely extended…
Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
Zhuoyan Luo, Fengyuan Shi, Yixiao Ge +3
The Open-MAGVIT2 project produces an open-source replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large codebook (i.e., codes), and achieves the state…
SEED-Story: Multimodal Long Story Generation with Large Language Model
Shuai Yang, Yuying Ge, Yang Li +4
With the remarkable advancements in image generation and open-form text generation, the creation of interleaved image-text content has become an increasingly intriguing field. Mult…
GrootVL: Tree Topology is All You Need in State Space Model
Yicheng Xiao, Lin Song, Shaoli Huang +5
The state space models, employing recursively propagated features, demonstrate strong representation capabilities comparable to Transformer models and superior efficiency. However,…
VoCo-LLaMA: Towards Vision Compression with Large Language Models
Xubing Ye, Yukang Gan, Xiaoke Huang +2
Vision-Language Models (VLMs) have achieved remarkable success in various multi-modal tasks, but they are often bottlenecked by the limited context window and high computational co…
SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing
Yuying Ge, Sijie Zhao, Chen Li +2
In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form lan…
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
Bohao Li, Yuying Ge, Yi Chen +3
Comprehending text-rich visual content is paramount for the practical application of Multimodal Large Language Models (MLLMs), since text-rich scenarios are ubiquitous in the real…
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
Yuying Ge, Sijie Zhao, Jinguo Zhu +6
The rapid evolution of multimodal foundation model has demonstrated significant progresses in vision-language understanding and generation, e.g., our previous work SEED-LLaMA. Howe…
ST-LLM: Large Language Models Are Effective Temporal Learners
Ruyang Liu, Chen Li, Haoran Tang +3
Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI inter…
YOLO-World: Real-Time Open-Vocabulary Object Detection
Tianheng Cheng, Lin Song, Yixiao Ge +3
The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However, their reliance on predefined and trained object categories…
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
Yiyuan Zhang, Xiaohan Ding, Kaixiong Gong +3
We propose to improve transformers of a specific modality with irrelevant data from other modalities, e.g., improve an ImageNet model with audio or point cloud datasets. We would l…
Supervised Fine-tuning in turn Improves Visual Foundation Models
Xiaohu Jiang, Yixiao Ge, Yuying Ge +3
Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning…
LLaMA Pro: Progressive LLaMA with Block Expansion
Chengyue Wu, Yukang Gan, Yixiao Ge +5
Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA. To this end, we propo…