Action Localization

A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs. The choice of convolutional architecture is motivated via a localized first-order approximation of spectral graph convolutions. The model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes.

A Graph Attention Network (GAT) is a neural network architecture that operates on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, a GAT enables (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. See here for an explanation by DGL.

The ARMA GNN layer implements a rational graph filter with a recursive approximation.

Class activation guide is a module which uses weak localization information from the instrument activation maps to guide the verb and target recognition. Image source: Nwoye et al.

A (2+1)D Convolution is a type of convolution used for action recognition convolutional neural networks, with a spatiotemporal volume. As opposed to applying a 3D Convolution over the entire volume, which can be computationally expensive and lead to overfitting, a (2+1)D convolution splits computation into two convolutions: a spatial 2D convolution followed by a temporal 1D convolution.

A R(2+1)D convolutional neural network is a network for action recognition that employs R(2+1)D convolutions in a ResNet inspired architecture. The use of these convolutions over regular 3D Convolutions reduces computational complexity, prevents overfitting, and introduces more non-linearities that allow for a better functional relationship to be modeled.

ChebNet involves a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs. Description from: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Social-STGCNN is a method for human trajectory prediction. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects.

Spatial & temporal attention combines the advantages of spatial attention and temporal attention as it adaptively selects both important regions and key frames. Some works compute temporal attention and spatial attention separately, while others produce joint spatio & temporal attention maps. Further works focusing on capturing pairwise relations.

Video-Audio-Text Transformer, or VATT, is a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, it takes raw signals as inputs and extracts multidimensional representations that are rich enough to benefit a variety of downstream tasks. VATT borrows the exact architecture from BERT and ViT except the layer of tokenization and linear projection reserved for each modality separately. The design follows the same spirit as ViT that makes the minimal changes to the architecture so that the learned model can transfer its weights to various frameworks and tasks. VATT linearly projects each modality into a feature vector and feeds it into a Transformer encoder. A semantically hierarchical common space is defined to account for the granularity of different modalities and noise contrastive estimation is employed to train the model.

G3D is a unified spatial-temporal graph convolutional operator that directly models cross-spacetime joint dependencies. It leverages dense cross-spacetime edges as skip connections for direct information propagation across the 3D spatial-temporal graph.

MotionNet is a system for joint perception and motion prediction based on a bird's eye view (BEV) map, which encodes the object category and motion information from 3D point clouds in each grid cell. MotionNet takes a sequence of LiDAR sweeps as input and outputs the bird's eye view (BEV) map. The backbone of MotionNet is a spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses.

Self-Supervised Temporal Domain Adaptation (SSTDA) is a method for action segmentation with self-supervised temporal domain adaptation. It contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics.

In human action recognition, each type of action generally only depends on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed. Motivated by these observations, Song et al. proposed a joint spatial and temporal attention network based on LSTM, to adaptively find discriminative features and keyframes. Its main attention-related components are a spatial attention sub-network, to select important regions, and a temporal attention sub-network, to select key frames. The spatial attention sub-network can be written as: \begin{align} s_{t} &= U_{s}\tanh(W_{xs}X_{t} + W_{hs}h_{t-1}^{s} + b_{si}) + b_{so} \end{align} \begin{align} \alpha_{t} &= \text{Softmax}(s_{t}) \end{align} \begin{align} Y_{t} &= \alpha_{t} X_{t} \end{align} where $X_{t}$ is the input feature at time $t$, $U_{s}$, $W_{hs}$, $b_{si}$, and $b_{so}$ are learnable parameters, and $h_{t-1}^{s}$ is the hidden state at step $t-1$. Note that use of the hidden state $h$ means the attention process takes temporal relationships into consideration. The temporal attention sub-network is similar to the spatial branch and produces its attention map using: \begin{align} \beta_{t} = \delta(W_{xp}X_{t} + W_{hp}h_{t-1}^{p} + b_{p}). \end{align} It adopts a ReLU function instead of a normalization function for ease of optimization. It also uses a regularized objective function to improve convergence. Overall, this paper presents a joint spatiotemporal attention method to focus on important joints and keyframes, with excellent results on the action recognition task.

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Approximating Spatiotemporal Representations Using a 2DCNN

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