102 citations · 570 across the 31 of their papers we have counts for
31 papers
Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation
Jialin Yuan, Jay Patravali, Hung Nguyen +2
Unsupervised Video Object Segmentation (UVOS) aims at discovering objects and tracking them through videos. For accurate UVOS, we observe if one can locate precise segment proposal…
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation
Ye Yu, Jialin Yuan, Gaurav Mittal +2
Video Object Segmentation (VOS) is fundamental to video understanding. Transformer-based methods show significant performance improvement on semi-supervised VOS. However, existing…
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
Yu-Ying Liu, Alexander Moreno, Maxwell A. Xu +7
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in…
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation
Jay Patravali, Gaurav Mittal, Ye Yu +2
We present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. MetaUVFS leverages over 550K unlabeled videos to train a two-stream 2D…
Space Time Recurrent Memory Network
Hung Nguyen, Chanho Kim, Fuxin Li
Transformers have recently been popular for learning and inference in the spatial-temporal domain. However, their performance relies on storing and applying attention to the featur…
Topology-Aware Segmentation Using Discrete Morse Theory
Xiaoling Hu, Yusu Wang, Li Fuxin +2
In the segmentation of fine-scale structures from natural and biomedical images, per-pixel accuracy is not the only metric of concern. Topological correctness, such as vessel conne…
Generative Particle Variational Inference via Estimation of Functional Gradients
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference…
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
Matthew L. Olson, Roli Khanna, Lawrence Neal +2
Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactua…
Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
Chanho Kim, Li Fuxin, Mazen Alotaibi +1
In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches betwee…
iGOS++: Integrated Gradient Optimized Saliency by Bilateral Perturbations
Saeed Khorram, Tyler Lawson, Fuxin Li
The black-box nature of the deep networks makes the explanation for "why" they make certain predictions extremely challenging. Saliency maps are one of the most widely-used local e…
Deep Variational Instance Segmentation
Jialin Yuan, Chao Chen, Li Fuxin
Instance Segmentation, which seeks to obtain both class and instance labels for each pixel in the input image, is a challenging task in computer vision. State-of-the-art algorithms…
Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform
Jun Li, Li Fuxin, Sinisa Todorovic
Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, w…
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
Wenxuan Wu, Zhiyuan Wang, Zhuwen Li +2
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped…
Visualizing Point Cloud Classifiers by Curvature Smoothing
Chen Ziwen, Wenxuan Wu, Zhongang Qi +1
Recently, several networks that operate directly on point clouds have been proposed. There is significant utility in understanding their mechanisms to classify point clouds, which…
Implicit Generative Modeling for Efficient Exploration
Neale Ratzlaff, Qinxun Bai, Li Fuxin +1
Efficient exploration remains a challenging problem in reinforcement learning, especially for those tasks where rewards from environments are sparse. A commonly used approach for e…
Counterfactual States for Atari Agents via Generative Deep Learning
Matthew L. Olson, Lawrence Neal, Fuxin Li +1
Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past…
Topology-Preserving Deep Image Segmentation
Xiaoling Hu, Li Fuxin, Dimitris Samaras +1
Segmentation algorithms are prone to make topological errors on fine-scale structures, e.g., broken connections. We propose a novel method that learns to segment with correct topol…
Visualizing Deep Networks by Optimizing with Integrated Gradients
Zhongang Qi, Saeed Khorram, Li Fuxin
Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular appro…
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
Xinyao Wang, Liefeng Bo, Li Fuxin
Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied…
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Neale Ratzlaff, Li Fuxin
Standard neural networks are often overconfident when presented with data outside the training distribution. We introduce HyperGAN, a new generative model for learning a distributi…
Interactive Naming for Explaining Deep Neural Networks: A Formative Study
Mandana Hamidi-Haines, Zhongang Qi, Alan Fern +2
We consider the problem of explaining the decisions of deep neural networks for image recognition in terms of human-recognizable visual concepts. In particular, given a test set of…
PointConv: Deep Convolutional Networks on 3D Point Clouds
Wenxuan Wu, Zhongang Qi, Li Fuxin
Unlike images which are represented in regular dense grids, 3D point clouds are irregular and unordered, hence applying convolution on them can be difficult. In this paper, we exte…
Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry
Rahul Sawhney, Fuxin Li, Henrik I. Christensen +1
We address the problems of measuring geometric similarity between 3D scenes, represented through point clouds or range data frames, and associating them. Our approach leverages mac…
Unifying Bilateral Filtering and Adversarial Training for Robust Neural Networks
Neale Ratzlaff, Li Fuxin
Recent analysis of deep neural networks has revealed their vulnerability to carefully structured adversarial examples. Many effective algorithms exist to craft these adversarial ex…