22 citations · 43 across the 11 of their papers we have counts for
9 papers · 1 filter
Natural Language Camera Movement Understanding
Yuwen Tan, Joey Huang, Jin Huang +2
Understanding camera movement in natural language is critical for training and evaluating video generation models, among other applications. However, we demonstrate that existing v…
LiteFrame: Efficient Vision Encoders Unlock Frame Scaling in Video LLMs
Jihwan Kim, Nikhil Parthasarathy, Danfeng Qin +5
The fundamental challenge in scaling Video Large Language Models (Video LLMs) to long-form video lies in managing the explosion of visual-token context length. Existing strategies…
Extending Video Masked Autoencoders to 128 frames
Nitesh Bharadwaj Gundavarapu, Luke Friedman, Raghav Goyal +8
Video understanding has witnessed significant progress with recent video foundation models demonstrating strong performance owing to self-supervised pre-training objectives; Masked…
Epsilon-VAE: Denoising as Visual Decoding
Long Zhao, Sanghyun Woo, Ziyu Wan +6
In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. For high-dimensional visual data,…
VideoPrism: A Foundational Visual Encoder for Video Understanding
Long Zhao, Nitesh B. Gundavarapu, Liangzhe Yuan +16
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus…
Instruct-Imagen: Image Generation with Multi-modal Instruction
Hexiang Hu, Kelvin C. K. Chan, Yu-Chuan Su +9
This paper presents instruct-imagen, a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce *multi-modal instruction* for image…
Distilling Vision-Language Models on Millions of Videos
Yue Zhao, Long Zhao, Xingyi Zhou +9
The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply…
Module-wise Adaptive Distillation for Multimodality Foundation Models
Chen Liang, Jiahui Yu, Ming-Hsuan Yang +5
Pre-trained multimodal foundation models have demonstrated remarkable generalizability but pose challenges for deployment due to their large sizes. One effective approach to reduci…
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
Lijun Yu, José Lezama, Nitesh B. Gundavarapu +13
While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation. To effec…