Temporal Residual Networks for Dynamic Scene Recognition

By Christoph Feichtenhofer, Axel Pinz, Richard P. Wildes. 2017

84 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns (2019, background): “[28] and [9] generalized the residual networks (ResNets) for the spatiotemporal domain for video action recognition and”
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification (2017, background): “Since then, many video classiﬁcation methods follow the same multi-stream 2D CNN design, and have made improvements in terms of new representations [25,26], different backbone architecture [27,28,29,17], fusion of the streams [30,31,32,33] and exploiting richer temporal structures [34,35,36].”
Video Representation Learning Using Discriminative Pooling (2018, methodology): “Successful methods following this approach are the two-stream models and their extensions [11, 12, 18, 20, 41].”
NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning (2018, background): “While approaches for action classification on presegmented video clips already perform convincingly well [27, 35, 4, 7], realistic applications require the segmentation of temporally untrimmed videos that usually contain a large variety of different actions with different lengths.”
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition (2018, background): “…in videos has received signiﬁcant research attention in the computer vision and machine learning community (Karpathy et al. 2014; Wang and Schmid 2013; Simonyan and Zisserman 2014; Fernando et al. 2015; Wang et al. 2016; Qiu, Yao, and Mei 2017; Carreira and Zisserman 2017; Shi et al. 2017).”
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition With CNNs (2019, methodology): “YUP++ [19] dataset contains so-called video textures.”
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition (2019, background): “Generally speaking, they can be divided into two directions, namely, (1) two-stage solutions [8, 1, 16, 29, 35] which extract spatial feature vectors from video frames and then integrate the obtained local feature sequence into one compact video descriptor for recognition; (2) the 2D [45, 36, 10, 7]”
Dynamic Inference: A New Approach Toward Efficient Video Action Recognition (2020, background): “Recently, significant progress has been achieved in terms of the recognition performance in this area following the deep convolutional network paradigm [30, 5, 4, 6, 26, 1, 8, 21].”
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition (2018, unknown): “CNN+RNN architecture for video sequence modelling [2, 3] and purely ConvNet-based video recognition [4, 5, 6, 7, 8, 1, 9] are two major research directions.”
Rethinking Spatiotemporal Feature Learning For Video Understanding (2017, background): “Since then, many works on video understanding follow the same multi-stream 2D CNN design, and have made improvements in terms of backbone architecture [10, 52, 30, 12], fusion of the streams [11, 8, 9, 58] and exploiting richer temporal structures [6, 54, 53].”