12 citations · 25 across the 13 of their papers we have counts for
13 papers
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…
Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement
Ping Hu, Simon Niklaus, Lu Zhang +2
In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirection…
Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
Ariel N. Lee, Sarah Adel Bargal, Janavi Kasera +3
Vision transformers (ViTs) have significantly changed the computer vision landscape and have periodically exhibited superior performance in vision tasks compared to convolutional n…
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Dina Bashkirova, Samarth Mishra, Diala Lteif +16
Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…
Sim-Anchored Learning for On-the-Fly Adaptation
Bassel El Mabsout, Shahin Roozkhosh, Siddharth Mysore +2
Fine-tuning simulation-trained RL agents with real-world data often degrades crucial behaviors due to limited or skewed data distributions. We argue that designer priorities exist…
Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing
Nataniel Ruiz, Sarah Adel Bargal, Cihang Xie +2
Modern deep neural networks tend to be evaluated on static test sets. One shortcoming of this is the fact that these deep neural networks cannot be easily evaluated for robustness…
Temporal Relevance Analysis for Video Action Models
Quanfu Fan, Donghyun Kim, Chun-Fu +4
In this paper, we provide a deep analysis of temporal modeling for action recognition, an important but underexplored problem in the literature. We first propose a new approach to…
Many-to-many Splatting for Efficient Video Frame Interpolation
Ping Hu, Simon Niklaus, Stan Sclaroff +1
Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant. Yet due to the inherent challenges of mo…
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee +17
Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of…
Learning to Detect Every Thing in an Open World
Kuniaki Saito, Ping Hu, Trevor Darrell +1
Many open-world applications require the detection of novel objects, yet state-of-the-art object detection and instance segmentation networks do not excel at this task. The key iss…
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
Samarth Mishra, Rameswar Panda, Cheng Perng Phoo +5
Pre-training models on Imagenet or other massive datasets of real images has led to major advances in computer vision, albeit accompanied with shortcomings related to curation cost…
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
Dina Bashkirova, Mohamed Abdelfattah, Ziliang Zhu +7
Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researcher…
Neural Parameter Allocation Search
Bryan A. Plummer, Nikoli Dryden, Julius Frost +2
Training neural networks requires increasing amounts of memory. Parameter sharing can reduce memory and communication costs, but existing methods assume networks have many identica…