163 citations · 452 across the 26 of their papers we have counts for
26 papers
Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency
Di Liu, Long Zhao, Qilong Zhangli +3
The task of shape abstraction with semantic part consistency is challenging due to the complex geometries of natural objects. Recent methods learn to represent an object shape usin…
A Parse-Then-Place Approach for Generating Graphic Layouts from Textual Descriptions
Jiawei Lin, Jiaqi Guo, Shizhao Sun +4
Creating layouts is a fundamental step in graphic design. In this work, we propose to use text as the guidance to create graphic layouts, i.e., Text-to-Layout, aiming to lower the…
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…
HGDNet: A Height-Hierarchy Guided Dual-Decoder Network for Single View Building Extraction and Height Estimation
Chaoran Lu, Ningning Cao, Pan Zhang +7
Unifying the correlative single-view satellite image building extraction and height estimation tasks indicates a promising way to share representations and acquire generalist model…
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone
Guozhang Liu, Baochai Peng, Ting Liu +7
The diversity of building architecture styles of global cities situated on various landforms, the degraded optical imagery affected by clouds and shadows, and the significant inter…
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…
Steering Prototypes with Prompt-tuning for Rehearsal-free Continual Learning
Zhuowei Li, Long Zhao, Zizhao Zhang +4
In the context of continual learning, prototypes-as representative class embeddings-offer advantages in memory conservation and the mitigation of catastrophic forgetting. However,…
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…
PERT: Pre-training BERT with Permuted Language Model
Yiming Cui, Ziqing Yang, Ting Liu
Pre-trained Language Models (PLMs) have been widely used in various natural language processing (NLP) tasks, owing to their powerful text representations trained on large-scale cor…
Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving
Jingxiao Zheng, Xinwei Shi, Alexander Gorban +9
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps,…
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…
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…
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness
Long Zhao, Ting Liu, Xi Peng +1
Adversarial data augmentation has shown promise for training robust deep neural networks against unforeseen data shifts or corruptions. However, it is difficult to define heuristic…
EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video
Jennifer J. Sun, Ting Liu, Alan S. Cowen +3
Videos can evoke a range of affective responses in viewers. The ability to predict evoked affect from a video, before viewers watch the video, can help in content creation and vide…
View-Invariant Probabilistic Embedding for Human Pose
Jennifer J. Sun, Jiaping Zhao, Liang-Chieh Chen +3
Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in h…
GLA in MediaEval 2018 Emotional Impact of Movies Task
Jennifer J. Sun, Ting Liu, Gautam Prasad
The visual and audio information from movies can evoke a variety of emotions in viewers. Towards a better understanding of viewer impact, we present our methods for the MediaEval 2…
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Bowen Cheng, Maxwell D. Collins, Yukun Zhu +4
In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achiev…
Panoptic-DeepLab
Bowen Cheng, Maxwell D. Collins, Yukun Zhu +4
We present Panoptic-DeepLab, a bottom-up and single-shot approach for panoptic segmentation. Our Panoptic-DeepLab is conceptually simple and delivers state-of-the-art results. In p…
DeeperLab: Single-Shot Image Parser
Tien-Ju Yang, Maxwell D. Collins, Yukun Zhu +6
We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for '…
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
Phuc Nguyen, Ting Liu, Gautam Prasad +1
We propose a weakly supervised temporal action localization algorithm on untrimmed videos using convolutional neural networks. Our algorithm learns from video-level class labels an…