An overview of violence detection techniques: current challenges and future directions
Abstract The Big Video Data generated in today's smart cities has raised concerns from its
purposeful usage perspective, where surveillance cameras, among many others are the …
purposeful usage perspective, where surveillance cameras, among many others are the …
Uniformer: Unifying convolution and self-attention for visual recognition
It is a challenging task to learn discriminative representation from images and videos, due to
large local redundancy and complex global dependency in these visual data. Convolution …
large local redundancy and complex global dependency in these visual data. Convolution …
Actionclip: A new paradigm for video action recognition
The canonical approach to video action recognition dictates a neural model to do a classic
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …
and standard 1-of-N majority vote task. They are trained to predict a fixed set of predefined …
Tdn: Temporal difference networks for efficient action recognition
Temporal modeling still remains challenging for action recognition in videos. To mitigate this
issue, this paper presents a new video architecture, termed as Temporal Difference Network …
issue, this paper presents a new video architecture, termed as Temporal Difference Network …
Movinets: Mobile video networks for efficient video recognition
Abstract We present Mobile Video Networks (MoViNets), a family of computation and
memory efficient video networks that can operate on streaming video for online inference …
memory efficient video networks that can operate on streaming video for online inference …
Long movie clip classification with state-space video models
MM Islam, G Bertasius - European Conference on Computer Vision, 2022 - Springer
Most modern video recognition models are designed to operate on short video clips (eg, 5–
10 s in length). Thus, it is challenging to apply such models to long movie understanding …
10 s in length). Thus, it is challenging to apply such models to long movie understanding …
Stand-alone inter-frame attention in video models
Motion, as the uniqueness of a video, has been critical to the development of video
understanding models. Modern deep learning models leverage motion by either executing …
understanding models. Modern deep learning models leverage motion by either executing …
Motion-driven visual tempo learning for video-based action recognition
Action visual tempo characterizes the dynamics and the temporal scale of an action, which is
helpful to distinguish human actions that share high similarities in visual dynamics and …
helpful to distinguish human actions that share high similarities in visual dynamics and …
Dynamic temporal filtering in video models
Video temporal dynamics is conventionally modeled with 3D spatial-temporal kernel or its
factorized version comprised of 2D spatial kernel and 1D temporal kernel. The modeling …
factorized version comprised of 2D spatial kernel and 1D temporal kernel. The modeling …
The dawn of quantum natural language processing
In this paper, we discuss the initial attempts at boosting understanding human language
based on deep-learning models with quantum computing. We successfully train a quantum …
based on deep-learning models with quantum computing. We successfully train a quantum …