477 citations · 935 across the 20 of their papers we have counts for
21 papers
Video Encoders Built on Image Representations
Jusheng Zhang, Wenhao Wang, Longqi Cai +3
The design of a video encoder determines when frames begin to interact and which frame-specific visual evidence remains accessible to the language model. Native video pathways coup…
Image Diffusion Preview with Consistency Solver
Fu-Yun Wang, Hao Zhou, Liangzhe Yuan +8
The slow inference process of image diffusion models significantly degrades interactive user experiences. To address this, we introduce Diffusion Preview, a novel paradigm employin…
Video Creation by Demonstration
Yihong Sun, Hao Zhou, Liangzhe Yuan +7
We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physical…
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…
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…
PolyMaX: General Dense Prediction with Mask Transformer
Xuan Yang, Liangzhe Yuan, Kimberly Wilber +8
Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or r…
Learning from Semantic Alignment between Unpaired Multiviews for Egocentric Video Recognition
Qitong Wang, Long Zhao, Liangzhe Yuan +2
We are concerned with a challenging scenario in unpaired multiview video learning. In this case, the model aims to learn comprehensive multiview representations while the cross-vie…
VideoGLUE: Video General Understanding Evaluation of Foundation Models
Liangzhe Yuan, Nitesh Bharadwaj Gundavarapu, Long Zhao +14
We evaluate the video understanding capabilities of existing foundation models (FMs) using a carefully designed experiment protocol consisting of three hallmark tasks (action recog…
Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding
Yuanhao Xiong, Long Zhao, Boqing Gong +5
Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grai…
Unified Visual Relationship Detection with Vision and Language Models
Long Zhao, Liangzhe Yuan, Boqing Gong +5
This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets cou…
Surrogate Gap Minimization Improves Sharpness-Aware Training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan +6
The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by minimizing a \textit{perturbed loss} defined as the maximum loss within a neighborhood in the pa…
Exploring Temporal Granularity in Self-Supervised Video Representation Learning
Rui Qian, Yeqing Li, Liangzhe Yuan +7
This work presents a self-supervised learning framework named TeG to explore Temporal Granularity in learning video representations. In TeG, we sample a long clip from a video and…
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
Liangzhe Yuan, Rui Qian, Yin Cui +5
Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video r…
DeepLab2: A TensorFlow Library for Deep Labeling
Mark Weber, Huiyu Wang, Siyuan Qiao +12
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer…
MoViNets: Mobile Video Networks for Efficient Video Recognition
Dan Kondratyuk, Liangzhe Yuan, Yandong Li +4
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference. 3D convolutional…
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
Hassan Akbari, Liangzhe Yuan, Rui Qian +4
We present a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer…
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Long Zhao, Yuxiao Wang, Jiaping Zhao +7
We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-vie…
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Ting Liu, Jennifer J. Sun, Long Zhao +6
Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos…
Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney +1
In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input…
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination
Liangzhe Yuan, Yibo Chen, Hantian Liu +2
We propose a light-weight video frame interpolation algorithm. Our key innovation is an instance-level supervision that allows information to be learned from the high-resolution ve…
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Alex Zihao Zhu, Liangzhe Yuan, Kenneth Chaney +1
Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, develop…