3.5k citations · 4.8k across the 85 of their papers we have counts for
22 papers · 1 filter
Video Salient Object Detection via Contrastive Features and Attention Modules
Yi-Wen Chen, Xiaojie Jin, Xiaohui Shen +1
Video salient object detection aims to find the most visually distinctive objects in a video. To explore the temporal dependencies, existing methods usually resort to recurrent neu…
Semi-supervised Multi-task Learning for Semantics and Depth
Yufeng Wang, Yi-Hsuan Tsai, Wei-Chih Hung +3
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. Typical MTL methods are jointly trained…
Toward Real-World Super-Resolution via Adaptive Downsampling Models
Sanghyun Son, Jaeha Kim, Wei-Sheng Lai +2
Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e.g.…
Federated Multi-Target Domain Adaptation
Chun-Han Yao, Boqing Gong, Yin Cui +3
Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quali…
Discovering 3D Parts from Image Collections
Chun-Han Yao, Wei-Chih Hung, Varun Jampani +1
Reasoning 3D shapes from 2D images is an essential yet challenging task, especially when only single-view images are at our disposal. While an object can have a complicated shape,…
Learning 3D Dense Correspondence via Canonical Point Autoencoder
An-Chieh Cheng, Xueting Li, Min Sun +2
We propose a canonical point autoencoder (CPAE) that predicts dense correspondences between 3D shapes of the same category. The autoencoder performs two key functions: (a) encoding…
End-to-end Multi-modal Video Temporal Grounding
Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang
We address the problem of text-guided video temporal grounding, which aims to identify the time interval of a certain event based on a natural language description. Different from…
Learning to Stylize Novel Views
Hsin-Ping Huang, Hung-Yu Tseng, Saurabh Saini +2
We tackle a 3D scene stylization problem - generating stylized images of a scene from arbitrary novel views given a set of images of the same scene and a reference image of the des…
COMISR: Compression-Informed Video Super-Resolution
Yinxiao Li, Pengchong Jin, Feng Yang +3
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression. However, most videos on the…
Decoupled Dynamic Filter Networks
Jingkai Zhou, Varun Jampani, Zhixiong Pi +2
Convolution is one of the basic building blocks of CNN architectures. Despite its common use, standard convolution has two main shortcomings: Content-agnostic and Computation-heavy…
2.5D Visual Relationship Detection
Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen +8
Visual 2.5D perception involves understanding the semantics and geometry of a scene through reasoning about object relationships with respect to the viewer in an environment. Howev…
Understanding Synonymous Referring Expressions via Contrastive Features
Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang
Referring expression comprehension aims to localize objects identified by natural language descriptions. This is a challenging task as it requires understanding of both visual and…
Weakly Supervised Object Localization and Detection: A Survey
Dingwen Zhang, Junwei Han, Gong Cheng +1
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation c…
Regularizing Generative Adversarial Networks under Limited Data
Hung-Yu Tseng, Lu Jiang, Ce Liu +2
Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This wor…
The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation
Yuankai Qi, Zizheng Pan, Yicong Hong +3
Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Mos…
In&Out : Diverse Image Outpainting via GAN Inversion
Yen-Chi Cheng, Chieh Hubert Lin, Hsin-Ying Lee +3
Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting -- filling in missing pixels in a way cohere…
Unsupervised Sound Localization via Iterative Contrastive Learning
Yan-Bo Lin, Hung-Yu Tseng, Hsin-Ying Lee +2
Sound localization aims to find the source of the audio signal in the visual scene. However, it is labor-intensive to annotate the correlations between the signals sampled from the…
ReMix: Towards Image-to-Image Translation with Limited Data
Jie Cao, Luanxuan Hou, Ming-Hsuan Yang +2
Image-to-image (I2I) translation methods based on generative adversarial networks (GANs) typically suffer from overfitting when limited training data is available. In this work, we…
Self-Attentive 3D Human Pose and Shape Estimation from Videos
Yun-Chun Chen, Marco Piccirilli, Robinson Piramuthu +1
We consider the task of estimating 3D human pose and shape from videos. While existing frame-based approaches have made significant progress, these methods are independently applie…
Exploiting Raw Images for Real-Scene Super-Resolution
Xiangyu Xu, Yongrui Ma, Wenxiu Sun +1
Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single ima…
Hybrid Neural Fusion for Full-frame Video Stabilization
Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang +2
Existing video stabilization methods often generate visible distortion or require aggressive cropping of frame boundaries, resulting in smaller field of views. In this work, we pre…
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
Xiangyu Xu, Muchen Li, Wenxiu Sun +1
Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explici…