3.5k citations · 4.8k across the 85 of their papers we have counts for
90 papers
Progressive Multi-resolution Loss for Crowd Counting
Ziheng Yan, Yuankai Qi, Guorong Li +4
Crowd counting is usually handled in a density map regression fashion, which is supervised via a L2 loss between the predicted density map and ground truth. To effectively regulate…
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
Chen Zhang, Guorong Li, Yuankai Qi +4
Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved signific…
Consistency-Aware Anchor Pyramid Network for Crowd Localization
Xinyan Liu, Guorong Li, Yuankai Qi +4
Crowd localization aims to predict the spatial position of humans in a crowd scenario. We observe that the performance of existing methods is challenged from two aspects: (i) ranki…
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Ling Yang, Zhilin Huang, Yang Song +6
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and the…
Learning Enriched Features for Fast Image Restoration and Enhancement
Syed Waqas Zamir, Aditya Arora, Salman Khan +4
Given a degraded input image, image restoration aims to recover the missing high-quality image content. Numerous applications demand effective image restoration, e.g., computationa…
Deep Image Deblurring: A Survey
Kaihao Zhang, Wenqi Ren, Wenhan Luo +4
Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significa…
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…