6 citations · 17 across the 11 of their papers we have counts for
11 papers
3D Point World Models: Point Completion Enables More Accurate Dynamics Learning
Skand Peri, Hung Nguyen, Chanho Kim +2
Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. However, large video-based dynami…
Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement
Minku Kim, Kuan-Chia Chen, Aayam Shrestha +3
We introduce \emph{Humanoid Hanoi}, a challenge problem for long-horizon humanoid skill composition that requires a robot to repeatedly sequence pickup, locomotion, and placement s…
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions
Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi +2
Despite extensive research on training generative adversarial networks (GANs) with limited training data, learning to generate images from long-tailed training distributions remain…
AutoFocusFormer: Image Segmentation off the Grid
Chen Ziwen, Kaushik Patnaik, Shuangfei Zhai +5
Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small object…
Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods
Mingqi Jiang, Saeed Khorram, Li Fuxin
In order to gain insights about the decision-making of different visual recognition backbones, we propose two methodologies, sub-explanation counting and cross-testing, that system…
Cycle-Consistent Counterfactuals by Latent Transformations
Saeed Khorram, Li Fuxin
CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either re…
From Heatmaps to Structural Explanations of Image Classifiers
Li Fuxin, Zhongang Qi, Saeed Khorram +4
This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained. The pape…
Stochastic Block-ADMM for Training Deep Networks
Saeed Khorram, Xiao Fu, Mohamad H. Danesh +2
In this paper, we propose Stochastic Block-ADMM as an approach to train deep neural networks in batch and online settings. Our method works by splitting neural networks into an arb…
Deep Convolution for Irregularly Sampled Temporal Point Clouds
Erich Merrill, Stefan Lee, Li Fuxin +2
We consider the problem of modeling the dynamics of continuous spatial-temporal processes represented by irregular samples through both space and time. Such processes occur in sens…
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions
Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern +1
Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, i…
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
Vivswan Shitole, Li Fuxin, Minsuk Kahng +2
Attention maps are a popular way of explaining the decisions of convolutional networks for image classification. Typically, for each image of interest, a single attention map is pr…