551 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH6 papers
- Walt Disney (Switzerland)CH5 papers
- Fudan UniversityCN3 papers
- California Institute of TechnologyUS2 papers
- Carnegie Mellon UniversityUS2 papers
- Columbia UniversityUS2 papers
- Dartmouth CollegeUS2 papers
- KTH Royal Institute of TechnologySE2 papers
- University of Colorado BoulderUS2 papers
- Adobe Systems (United States)US1 paper
- AMA Computer UniversityPH1 paper
- Apple (United States)US1 paper
20 papers · 1 filter
Deformable Wiener Filter for Future Video Coding
Xuewei Meng, Chuanmin Jia, Xinfeng Zhang +2
In-loop filters have attracted increasing attention due to the remarkable noise-reduction capability in the hybrid video coding framework. However, the existing in-loop filters in…
Sampling Based Scene-Space Video Processing
Felix Klose, Oliver Wang, Jean-Charles Bazin +2
Many compelling video processing effects can be achieved if per-pixel depth information and 3D camera calibrations are known. However, the success of such methods is highly depende…
Content Adaptive Optimization for Neural Image Compression
Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah +1
The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, s…
Globally Continuous and Non-Markovian Activity Analysis from Videos
He Wang, Carol O'Sullivan
Automatically recognizing activities in video is a classic problem in vision and helps to understand behaviors, describe scenes and detect anomalies. We propose an unsupervised met…
Deep Generative Video Compression
Jun Han, Salvator Lombardo, Christopher Schroers +1
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here…
Unsupervised Deep Representations for Learning Audience Facial Behaviors
Suman Saha, Rajitha Navarathna, Leonhard Helminger +1
In this paper, we present an unsupervised learning approach for analyzing facial behavior based on a deep generative model combined with a convolutional neural network (CNN). We jo…
Weakly-supervised Visual Grounding of Phrases with Linguistic Structures
Fanyi Xiao, Leonid Sigal, Yong Jae Lee
We propose a weakly-supervised approach that takes image-sentence pairs as input and learns to visually ground (i.e., localize) arbitrary linguistic phrases, in the form of spatial…
Semi-Latent GAN: Learning to generate and modify facial images from attributes
Weidong Yin, Yanwei Fu, Leonid Sigal +1
Generating and manipulating human facial images using high-level attributal controls are important and interesting problems. The models proposed in previous work can solve one of t…
Weakly-Supervised Spatial Context Networks
Zuxuan Wu, Larry S. Davis, Leonid Sigal
We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a…
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models
Yu-ting Qiang, Yanwei Fu, Xiao Yu +3
Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generati…
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung +1
We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level pro…
Panoptic Studio: A Massively Multiview System for Social Interaction Capture
Hanbyul Joo, Tomas Simon, Xulong Li +10
We present an approach to capture the 3D motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is fun…
Learning Video Object Segmentation from Static Images
Anna Khoreva, Federico Perazzi, Rodrigo Benenson +2
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentati…
Semi-supervised Vocabulary-informed Learning
Yanwei Fu, Leonid Sigal
Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to r…
Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web
Shugao Ma, Sarah Adel Bargal, Jianming Zhang +2
Recently, attempts have been made to collect millions of videos to train CNN models for action recognition in videos. However, curating such large-scale video datasets requires imm…
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization
Baohan Xu, Yanwei Fu, Yu-Gang Jiang +2
Emotion is a key element in user-generated videos. However, it is difficult to understand emotions conveyed in such videos due to the complex and unstructured nature of user-genera…
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
Xi Zhang, Yanwei Fu, Shanshan Jiang +2
Learning from synthetic data has many important and practical applications. An example of application is photo-sketch recognition. Using synthetic data is challenging due to the di…
Transductive Multi-view Zero-Shot Learning
Yanwei Fu, Timothy M. Hospedales, Tao Xiang +1
Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation shared between an annotated auxiliary dataset and a target d…
A Unified Semantic Embedding: Relating Taxonomies and Attributes
Sung Ju Hwang, Leonid Sigal
We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attribute…
Multi-view Metric Learning for Multi-view Video Summarization
Yanwei Fu, Lingbo Wang, Yanwen Guo
Traditional methods on video summarization are designed to generate summaries for single-view video records; and thus they cannot fully exploit the redundancy in multi-view video r…