Activity Recognition

Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. For pre-training, CLIP is trained to predict which of the $N X N$ possible (image, text) pairings across a batch actually occurred. CLIP learns a multi-modal embedding space by jointly training an image encoder and text encoder to maximize the cosine similarity of the image and text embeddings of the $N$ real pairs in the batch while minimizing the cosine similarity of the embeddings of the $N^2 - N$ incorrect pairings. A symmetric cross entropy loss is optimized over these similarity scores. Image credit: Learning Transferable Visual Models From Natural Language Supervision

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.

A Non-Local Operation is a component for capturing long-range dependencies with deep neural networks. It is a generalization of the classical non-local mean operation in computer vision. Intuitively a non-local operation computes the response at a position as a weighted sum of the features at all positions in the input feature maps. The set of positions can be in space, time, or spacetime, implying that these operations are applicable for image, sequence, and video problems. Following the non-local mean operation, a generic non-local operation for deep neural networks is defined as: $$ \mathbb{y}\_{i} = \frac{1}{\mathcal{C}\left(\mathbb{x}\right)}\sum\_{\forall{j}}f\left(\mathbb{x}\_{i}, \mathbb{x}\_{j}\right)g\left(\mathbb{x}\_{j}\right) $$ Here $i$ is the index of an output position (in space, time, or spacetime) whose response is to be computed and $j$ is the index that enumerates all possible positions. x is the input signal (image, sequence, video; often their features) and $y$ is the output signal of the same size as $x$. A pairwise function $f$ computes a scalar (representing relationship such as affinity) between $i$ and all $j$. The unary function $g$ computes a representation of the input signal at the position $j$. The response is normalized by a factor $C\left(x\right)$. The non-local behavior is due to the fact that all positions ($\forall{j}$) are considered in the operation. As a comparison, a convolutional operation sums up the weighted input in a local neighborhood (e.g., $i − 1 \leq j \leq i + 1$ in a 1D case with kernel size 3), and a recurrent operation at time $i$ is often based only on the current and the latest time steps (e.g., $j = i$ or $i − 1$). The non-local operation is also different from a fully-connected (fc) layer. The equation above computes responses based on relationships between different locations, whereas fc uses learned weights. In other words, the relationship between $x\_{j}$ and $x\_{i}$ is not a function of the input data in fc, unlike in nonlocal layers. Furthermore, the formulation in the equation above supports inputs of variable sizes, and maintains the corresponding size in the output. On the contrary, an fc layer requires a fixed-size input/output and loses positional correspondence (e.g., that from $x\_{i}$ to $y\_{i}$ at the position $i$). A non-local operation is a flexible building block and can be easily used together with convolutional/recurrent layers. It can be added into the earlier part of deep neural networks, unlike fc layers that are often used in the end. This allows us to build a richer hierarchy that combines both non-local and local information. In terms of parameterisation, we usually parameterise $g$ as a linear embedding of the form $g\left(x\_{j}\right) = W\_{g}\mathbb{x}\_{j}$ , where $W\_{g}$ is a weight matrix to be learned. This is implemented as, e.g., 1×1 convolution in space or 1×1×1 convolution in spacetime. For $f$ we use an affinity function, a list of which can be found here.

A Non-Local Block is an image block module used in neural networks that wraps a non-local operation. We can define a non-local block as: $$ \mathbb{z}\_{i} = W\_{z}\mathbb{y\_{i}} + \mathbb{x}\_{i} $$ where $y\_{i}$ is the output from the non-local operation and $+ \mathbb{x}\_{i}$ is a residual connection.

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

TAM is designed to capture complex temporal relationships both efficiently and flexibly, It adopts an adaptive kernel instead of self-attention to capture global contextual information, with lower time complexity than GLTR. TAM has two branches, a local branch and a global branch. Given the input feature map $X\in \mathbb{R}^{C\times T\times H\times W}$, global spatial average pooling $\text{GAP}$ is first applied to the feature map to ensure TAM has a low computational cost. Then the local branch in TAM employs several 1D convolutions with ReLU nonlinearity across the temporal domain to produce location-sensitive importance maps for enhancing frame-wise features. The local branch can be written as \begin{align} s &= \sigma(\text{Conv1D}(\delta(\text{Conv1D}(\text{GAP}(X))))) \end{align} \begin{align} X^1 &= s X \end{align} Unlike the local branch, the global branch is location invariant and focuses on generating a channel-wise adaptive kernel based on global temporal information in each channel. For the $c$-th channel, the kernel can be written as \begin{align} \Theta_c = \text{Softmax}(\text{FC}_2(\delta(\text{FC}_1(\text{GAP}(X)c)))) \end{align} where $\Theta_c \in \mathbb{R}^{K}$ and $K$ is the adaptive kernel size. Finally, TAM convolves the adaptive kernel $\Theta$ with $ X\text{out}^1$: \begin{align} Y = \Theta \otimes X^1 \end{align} With the help of the local branch and global branch, TAM can capture the complex temporal structures in video and enhance per-frame features at low computational cost. Due to its flexibility and lightweight design, TAM can be added to any existing 2D CNNs.

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.

TimeSformer is a convolution-free approach to video classification built exclusively on self-attention over space and time. It adapts the standard Transformer architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Specifically, the method adapts the image model [Vision Transformer](https//www.paperswithcode.com/method/vision-transformer) (ViT) to video by extending the self-attention mechanism from the image space to the space-time 3D volume. As in ViT, each patch is linearly mapped into an embedding and augmented with positional information. This makes it possible to interpret the resulting sequence of vector

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.

Florence is a computer vision foundation model aiming to learn universal visual-language representations that be adapted to various computer vision tasks, visual question answering, image captioning, video retrieval, among other tasks. Florence's workflow consists of data curation, unified learning, Transformer architectures and adaption. Florence is pre-trained in an image-label-description space, utilizing a unified image-text contrastive learning. It involves a two-tower architecture: 12-layer Transformer for the language encoder, and a Vision Transformer for the image encoder. Two linear projection layers are added on top of the image encoder and language encoder to match the dimensions of image and language features. Compared to previous methods for cross-modal shared representations, Florence expands beyond simple classification and retrieval capabilities to advanced representations that support object level, multiple modality, and videos respectively.

Multiscale Vision Transformer, or MViT, is a transformer architecture for modeling visual data such as images and videos. Unlike conventional transformers, which maintain a constant channel capacity and resolution throughout the network, Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features.

Embedded Gaussian Affinity is a type of affinity or self-similarity function between two points $\mathbf{x\_{i}}$ and $\mathbf{x\_{j}}$ that uses a Gaussian function in an embedding space: $$ f\left(\mathbf{x\_{i}}, \mathbf{x\_{j}}\right) = e^{\theta\left(\mathbf{x\_{i}}\right)^{T}\phi\left(\mathbf{x\_{j}}\right)} $$ Here $\theta\left(x\_{i}\right) = W\_{θ}x\_{i}$ and $\phi\left(x\_{j}\right) = W\_{φ}x\_{j}$ are two embeddings. Note that the self-attention module used in the original Transformer model is a special case of non-local operations in the embedded Gaussian version. This can be seen from the fact that for a given $i$, $\frac{1}{\mathcal{C}\left(\mathbf{x}\right)}\sum\_{\forall{j}}f\left(\mathbf{x}\_{i}, \mathbf{x}\_{j}\right)g\left(\mathbf{x}\_{j}\right)$ becomes the softmax computation along the dimension $j$. So we have $\mathbf{y} = \text{softmax}\left(\mathbf{x}^{T}W^{T}\_{\theta}W\_{\phi}\mathbf{x}\right)g\left(\mathbf{x}\right)$, which is the self-attention form in the Transformer model. This shows how we can relate this recent self-attention model to the classic computer vision method of non-local means.

TDN, or Temporaral Difference Network, is an action recognition model that aims to capture multi-scale temporal information. To fully capture temporal information over the entire video, the TDN is established with a two-level difference modeling paradigm. Specifically, for local motion modeling, temporal difference over consecutive frames is used to supply 2D CNNs with finer motion pattern, while for global motion modeling, temporal difference across segments is incorporated to capture long-range structure for motion feature excitation.

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

Temporal Pyramid Network, or TPN, is a pyramid level module for action recognition at the feature-level, which can be flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner. The source of features and the fusion of features form a feature hierarchy for the backbone so that it can capture action instances at various tempos. In the TPN, a Backbone Network is used to extract multiple level features, a Spatial Semantic Modulation spatially downsamples features to align semantics, a Temporal Rate Modulation temporally downsamples features to adjust relative tempo among levels, Information Flow aggregates features in various directions to enhance and enrich level-wise representations and Final Prediction rescales and concatenates all levels of pyramid along channel dimension.

Contrastive Multiview Coding (CMC) is a self-supervised learning approach, based on CPC, that learns representations that capture information shared between multiple sensory views. The core idea is to set an anchor view and the sample positive and negative data points from the other view and maximise agreement between positive pairs in learning from two views. Contrastive learning is used to build the embedding.

Contrastive Video Representation Learning, or CVRL, is a self-supervised contrastive learning framework for learning spatiotemporal visual representations from unlabeled videos. Representations are learned using a contrastive loss, where two clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away. Data augmentations are designed involving spatial and temporal cues. Concretely, a temporally consistent spatial augmentation method is used to impose strong spatial augmentations on each frame of the video while maintaining the temporal consistency across frames. A sampling-based temporal augmentation method is also used to avoid overly enforcing invariance on clips that are distant in time. End-to-end, from a raw video, we first sample a temporal interval from a monotonically decreasing distribution. The temporal interval represents the number of frames between the start points of two clips, and we sample two clips from a video according to this interval. Afterwards we apply a temporally consistent spatial augmentation to each of the clips and feed them into a 3D backbone with an MLP head. The contrastive loss is used to train the network to attract the clips from the same video and repel the clips from different videos in the embedding space.

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.

CV-MIM, or Contrastive Cross-View Mutual Information Maximization, is a representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization, which maximizes mutual information of the same pose performed from different viewpoints in a contrastive learning manner. It further utilizes two regularization terms to ensure disentanglement and smoothness of the learned representations.

CAGAM is a form of spatial attention mechanism that propagates attention from a known to an unknown context features thereby enhancing the unknown context for relevant pattern discovery. Usually the known context feature is a class activation map (CAM).

Deflation is a video-to-image operation to transform a video network into a network that can ingest a single image. In the two types of video networks considered in the original paper, this deflation corresponds to the following operations: for 3D convolutional based networks, summing the 3D spatio-temporal filters over the temporal dimension to obtain 2D filters; for TSM networks,, turning off the channel shifting which results in a standard residual architecture (ResNet50) for images.

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.

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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