12 citations · 31 across the 22 of their papers we have counts for
15 papers · 1 filter
Motion Marionette: Rethinking Rigid Motion Transfer via Prior Guidance
Haoxuan Wang, Jiachen Tao, Junyi Wu +3
We present Motion Marionette, a zero-shot framework for rigid motion transfer from monocular source videos to single-view target images. Previous works typically employ geometric,…
Efficient Multimodal Dataset Distillation via Generative Models
Zhenghao Zhao, Haoxuan Wang, Junyi Wu +3
Dataset distillation aims to synthesize a small dataset from a large dataset, enabling the model trained on it to perform well on the original dataset. With the blooming of large l…
CaO: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation
Haoxuan Wang, Zhenghao Zhao, Junyi Wu +3
The recent introduction of diffusion models in dataset distillation has shown promising potential in creating compact surrogate datasets for large, high-resolution target datasets,…
SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding
Weitai Kang, Gaowen Liu, Mubarak Shah +1
Different from Object Detection, Visual Grounding deals with detecting a bounding box for each text-image pair. This one box for each text-image data provides sparse supervision si…
Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
Ziheng Chen, Yue Song, Xiao-Jun Wu +2
Global Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations.…
Efficient Multitask Dense Predictor via Binarization
Yuzhang Shang, Dan Xu, Gaowen Liu +2
Multi-task learning for dense prediction has emerged as a pivotal area in computer vision, enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks.…
Urban Scene Diffusion through Semantic Occupancy Map
Junge Zhang, Qihang Zhang, Li Zhang +3
Generating unbounded 3D scenes is crucial for large-scale scene understanding and simulation. Urban scenes, unlike natural landscapes, consist of various complex man-made objects a…
Causal-DFQ: Causality Guided Data-free Network Quantization
Yuzhang Shang, Bingxin Xu, Gaowen Liu +2
Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devi…
Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models
Peihao Chen, Xinyu Sun, Hongyan Zhi +5
We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by langu…
Optical Flow Estimation in 360 Videos: Dataset, Model and Application
Bin Duan, Keshav Bhandari, Gaowen Liu +1
Optical flow estimation has been a long-lasting and fundamental problem in the computer vision community. However, despite the advances of optical flow estimation in perspective vi…
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation
Keshav Bhandari, Bin Duan, Gaowen Liu +3
Optical flow estimation in omnidirectional videos faces two significant issues: the lack of benchmark datasets and the challenge of adapting perspective video-based methods to acco…
Online Deep Metric Learning via Mutual Distillation
Gao-Dong Liu, Wan-Lei Zhao, Jie Zhao
Deep metric learning aims to transform input data into an embedding space, where similar samples are close while dissimilar samples are far apart from each other. In practice, samp…
Cross-View Exocentric to Egocentric Video Synthesis
Gaowen Liu, Hao Tang, Hugo Latapie +2
Cross-view video synthesis task seeks to generate video sequences of one view from another dramatically different view. In this paper, we investigate the exocentric (third-person)…
Exocentric to Egocentric Image Generation via Parallel Generative Adversarial Network
Gaowen Liu, Hao Tang, Hugo Latapie +1
Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper, we investigate exocentric (third-pers…
Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation
Hao Tang, Dan Xu, Gaowen Liu +3
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (CGAN) for the task of keypoint-guided image generation. The proposed CGAN is a cross-modal f…