493 citations · 702 across the 20 of their papers we have counts for
21 papers
Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models
Matthew Kowal, Richard P. Wildes, Konstantinos G. Derpanis
Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vi…
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition
Filip Ilic, He Zhao, Thomas Pock +1
Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through o…
Understanding Video Transformers for Segmentation: A Survey of Application and Interpretability
Rezaul Karim, Richard P. Wildes
Video segmentation encompasses a wide range of categories of problem formulation, e.g., object, scene, actor-action and multimodal video segmentation, for delineating task-specific…
Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation
Mennatullah Siam, Rezaul Karim, He Zhao +1
Few-shot video segmentation is the task of delineating a specific novel class in a query video using few labelled support images. Typical approaches compare support and query featu…
StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos
Nikita Dvornik, Isma Hadji, Ran Zhang +4
Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, wi…
MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer
Rezaul Karim, He Zhao, Richard P. Wildes +1
In this paper, we present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video. The presented Multiscale Encode…
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
Matthew Kowal, Mennatullah Siam, Md Amirul Islam +3
There is limited understanding of the information captured by deep spatiotemporal models in their intermediate representations. For example, while evidence suggests that action rec…
Sports Video Analysis on Large-Scale Data
Dekun Wu, He Zhao, Xingce Bao +1
This paper investigates the modeling of automated machine description on sports video, which has seen much progress recently. Nevertheless, state-of-the-art approaches fall quite s…
Is Appearance Free Action Recognition Possible?
Filip Ilic, Thomas Pock, Richard P. Wildes
Intuition might suggest that motion and dynamic information are key to video-based action recognition. In contrast, there is evidence that state-of-the-art deep-learning video unde…
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
Matthew Kowal, Mennatullah Siam, Md Amirul Islam +3
Deep spatiotemporal models are used in a variety of computer vision tasks, such as action recognition and video object segmentation. Currently, there is a limited understanding of…
P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision
He Zhao, Isma Hadji, Nikita Dvornik +3
In this paper, we study the problem of procedure planning in instructional videos. Here, an agent must produce a plausible sequence of actions that can transform the environment fr…
Review of Video Predictive Understanding: Early Action Recognition and Future Action Prediction
He Zhao, Richard P. Wildes
Video predictive understanding encompasses a wide range of efforts that are concerned with the anticipation of the unobserved future from the current as well as historical video ob…
Interpretable Deep Feature Propagation for Early Action Recognition
He Zhao, Richard P. Wildes
Early action recognition (action prediction) from limited preliminary observations plays a critical role for streaming vision systems that demand real-time inference, as video acti…
Detecting Biological Locomotion in Video: A Computational Approach
Soo Min Kang, Richard P. Wildes
Animals locomote for various reasons: to search for food, find suitable habitat, pursue prey, escape from predators, or seek a mate. The grand scale of biodiversity contributes to…
Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support
Isma Hadji, Richard P. Wildes
It has been repeatedly observed that convolutional architectures when applied to image understanding tasks learn oriented bandpass filters. A standard explanation of this result is…
Graph Neural Net using Analytical Graph Filters and Topology Optimization for Image Denoising
Weng-tai Su, Gene Cheung, Richard Wildes +1
While convolutional neural nets (CNNs) have achieved remarkable performance for a wide range of inverse imaging applications, the filter coefficients are computed in a purely data-…
What Do We Understand About Convolutional Networks?
Isma Hadji, Richard P. Wildes
This document will review the most prominent proposals using multilayer convolutional architectures. Importantly, the various components of a typical convolutional network will be…
What have we learned from deep representations for action recognition?
Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes +1
As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they a…
A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition
Isma Hadji, Richard P. Wildes
This paper presents a novel hierarchical spatiotemporal orientation representation for spacetime image analysis. It is designed to combine the benefits of the multilayer architectu…
Spatiotemporal Residual Networks for Video Action Recognition
Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes
Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new techni…
Review of Action Recognition and Detection Methods
Soo Min Kang, Richard P. Wildes
In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in spa…