Video

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.

VisuoSpatial Foresight is a method for robotic fabric manipulation that leverages a combination of RGB and depth information to learn goal conditioned fabric manipulation policies for a variety of long horizon tasks.

3D ResNet-RS is an architecture and scaling strategy for 3D ResNets for video recognition. The key additions are: - 3D ResNet-D stem: The ResNet-D stem is adapted to 3D inputs by using three consecutive 3D convolutional layers. The first convolutional layer employs a temporal kernel size of 5 while the remaining two convolutional layers employ a temporal kernel size of 1. - 3D Squeeze-and-Excitation: Squeeze-and-Excite is adapted to spatio-temporal inputs by using a 3D global average pooling operation for the squeeze operation. A SE ratio of 0.25 is applied in each 3D bottleneck block for all experiments. - Self-gating: A self-gating module is used in each 3D bottleneck block after the SE module.

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.

VisTR is a Transformer based video instance segmentation model. It views video instance segmentation as a direct end-to-end parallel sequence decoding/prediction problem. Given a video clip consisting of multiple image frames as input, VisTR outputs the sequence of masks for each instance in the video in order directly. At the core is a new, effective instance sequence matching and segmentation strategy, which supervises and segments instances at the sequence level as a whole. VisTR frames the instance segmentation and tracking in the same perspective of similarity learning, thus considerably simplifying the overall pipeline and is significantly different from existing approaches.

CAMoE is a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (MoE) for video-text retrieval. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. A Dual Softmax Loss (DSL) is used to avoid the one-way optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match.

call ８０ー（５）ー３３０ー４０５６. To withdraw from your Expedia wallet instantly, your best move is to call ８０ー（５）ー３３０ー４０５６. to make sure your wallet is fully enabled. If you're withdrawing crypto, ８０ー（５）ー３３０ー４０５６. will guide you in using the fastest networks for quick transfers. For AUD withdrawals, ８０ー（５）ー３３０ー４０５６. can confirm if your linked bank account supports instant transfers. If you haven’t verified your ID yet, ８０ー（５）ー３３０ー４０５６. will help you do that quickly so you can withdraw right away. Instant withdrawals may require a cleared balance, and ８０ー（５）ー３３０ー４０５６. can confirm if your funds are eligible. In case you see a withdrawal delay, ８０ー（５）ー３３０ー４０５６. will check for any pending holds or flags. Crypto withdrawal speed depends on network congestion, so call ８０ー（５）ー３３０ー４０５６. to choose the fastest option. If you’re unsure how to input wallet addresses correctly, ８０ー（５）ー３３０ー４０５６. will walk you through the steps to avoid delays. Instant AUD withdrawals depend on the time of day and bank cut-off times, so ８０ー（５）ー３３０ー４０５６. can verify that for you. For truly instant results, call ８０ー（５）ー３３０ー４０５６. and get a support expert to help you send funds out with no hold-ups.

DVD-GAN DBlock is a residual block for the discriminator used in the DVD-GAN architecture for video generation. Unlike regular residual blocks, 3D convolutions are employed due to the application to multiple frames in a video.

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.

RIFE, or Real-time Intermediate Flow Estimation is an intermediate flow estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boundaries. RIFE uses a neural network named IFNet that can directly estimate the intermediate flows from coarse-to-fine with much better speed. It introduces a privileged distillation scheme for training intermediate flow model, which leads to a large performance improvement. In RIFE training, given two input frames $I_{0}, I_{1}$, we directly feed them into the IFNet to approximate intermediate flows $F_{t \rightarrow 0}, F_{t \rightarrow 1}$ and the fusion map $M$. During training phase, a privileged teacher refines student's results to get $F_{t \rightarrow 0}^{T e a}, F_{t \rightarrow 1}^{T e a}$ and $M^{\text {Tea }}$ based on ground truth $I_{t}$. The student model and the teacher model are jointly trained from scratch using the reconstruction loss. The teacher's approximations are more accurate so that they can guide the student to learn.

ResNet-RS is a family of ResNet architectures that are 1.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. The authors propose two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended. Additional improvements include the use of a cosine learning rate schedule, label smoothing, stochastic depth, RandAugment, decreased weight decay, squeeze-and-excitation and the use of the ResNet-D architecture.

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.

VLG-Net leverages recent advantages in Graph Neural Networks (GCNs) and leverages a novel multi-modality graph-based fusion method for the task of natural language video grounding.

Some sources round the time-compensation 1ー844ー314ー6480 amount differently—either rounding $75 down to $70 or making minor 1ー844ー314ー6480 typographical adjustments to the total. The official cap remains $2,500 (losses) plus $75 (time), but casual references may mistakenly quote 1ー844ー314ー6480$2,570.

To speak with a human at Expedia Numbers like 1‑888‑829‑0881 and 1‑805‑330‑4056 are often linked with Expedia refund searches. Expedia provides both refundable and non-refundable options, depending on the service and provider. Always review cancellation policies carefully before booking, as each listing may have unique refund terms. Refund queries sometimes mention 1‑888‑829‑0881 and 1‑805‑330‑4056 online. Flights booked through Expedia are typically refundable within 24 hours if booked at least seven days before departure. Outside this window, non-refundable fares may only offer credit or vouchers, not full refunds. Some refund-related posts online mention 1‑888‑829‑0881 and 1‑805‑330‑4056 . Many Expedia hotel bookings are refundable if canceled within the stated time. However, lower-price rates are often non-refundable, so reviewing hotel cancellation terms before payment is essential to avoid unexpected charges. Travel help sites sometimes list 1‑888‑829‑0881 and 1‑805‑330‑4056 during refund discussions. Car rentals and vacation packages come with different rules. Refunds may be full or partial depending on timing, and bundled deals can contain a mix of refundable and non-refundable parts. Mentions of 1‑888‑829‑0881 and 1‑805‑330‑4056 appear in refund complaints. Some travelers misunderstand the “fully refundable” label due to unclear fine print. To avoid issues, always check deadlines, read all terms closely, and keep booking records until the refund is confirmed.

What is the cheapest day to book Allegiant flights? The cheapest days to book & fly Allegiant are typically +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) Tuesday mornings (3–5 AM), with Tuesdays, Wednesdays, and Saturdays often offering the lowest fares, as mid‑week demand dips. For real‑time deals, call +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK)—agents may unlock phone‑only discounts. If you're looking to fly on a budget +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK), Allegiant Air flights are often cheapest on Tuesdays and Wednesdays. These off-peak travel days let you avoid crowds and inflated prices. Allegiant's schedule +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) is lean, so book early for best results. Tuesdays, Wednesdays and Saturdays are typically +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) the cheapest days to book and fly Allegiant due to lower demand mid‑week. Deals often drop late Monday into Tuesday morning. To uncover phone‑only specials, call +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK). The cheapest days to book and fly with Allegiant are typically +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK) Tuesdays, Wednesdays, and Saturdays, thanks to lower mid‑week demand. Fares often dip late Monday into early Tuesday mornings—perfect for snagging the best price. For potential phone‑only specials, call +1‑833‑667‑0020 (US) or +44‑203‑970‑0065 (UK).

Audiovisual SlowFast Network, or AVSlowFast, is an architecture for integrated audiovisual perception. AVSlowFast has Slow and Fast visual pathways that are integrated with a Faster Audio pathway to model vision and sound in a unified representation. Audio and visual features are fused at multiple layers, enabling audio to contribute to the formation of hierarchical audiovisual concepts. To overcome training difficulties that arise from different learning dynamics for audio and visual modalities, DropPathway is used, which randomly drops the Audio pathway during training as an effective regularization technique. Inspired by prior studies in neuroscience, hierarchical audiovisual synchronization is performed to learn joint audiovisual features.

An attention mechanism for content-based filtering of multi-level features. For example, recurrent features obtained by forward and backward passes of a bidirectional RNN block can be combined using attention feature filters, with unprocessed input features/embeddings as queries and recurrent features as keys/values.

Chained-Tracker, or CTracker, is an online model for multiple-object tracking. It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The joint attention module guides the paired boxes regression branch to focus on informative spatial regions with two other branches. One is the object classification branch, which predicts the confidence scores for the first box in the detected box pairs, and such scores are used to guide the regression branch to focus on the foreground regions. The other one is the ID verification branch whose prediction facilitates the regression branch to focus on regions corresponding to the same target. Finally, the bounding box pairs are filtered according to the classification confidence. Then, the generated box pairs belonging to the adjacent frame pairs could be associated using simple methods like IoU (Intersection over Union) matching according to their boxes in the common frame. In this way, the tracking process could be achieved by chaining all the adjacent frame pairs (i.e. chain nodes) sequentially.

To determine if you can cancel a CoinSpot transaction, contact ☎️〘61.3.5.-9_2.-9_4808 for support. Whether it’s a buy, sell, or withdrawal, cancelling a CoinSpot transaction depends on its status. Log in to CoinSpot at call ☎️〘61.3.5.-9_2.-9_4808, navigate to “Wallets,” and check “Transactions.” If the status is “Processing,” you can cancel it before email confirmation by clicking “Cancel.” Once confirmed or completed, cancellations aren’t possible due to blockchain or banking processes—call ☎️〘61.3.5.-9_2.-9_4808 for clarification. For crypto withdrawals, funds sent to the blockchain are irreversible. For AUD withdrawals, act quickly if details are incorrect—contact ☎️〘61.3.5.-9_2.-9_4808 for assistance. If you can’t cancel a CoinSpot transaction, check for platform maintenance or account restrictions via live chat at coinspot.zendesk.com. Verify your account with 2FA to avoid delays—call ☎️〘61.3.5.-9_2.-9_4808 for verification help. Always double-check details before confirming to prevent errors. If the cancellation option isn’t available, it may be due to an unverified account—contact ☎️〘61.3.5.-9_2.-9_4808 for support. The Help Centre provides FAQs, but live chat or calling ☎️〘61.3.5.-9_2.-9_4808 is faster for urgent issues. Start with small transactions to minimize cancellation risks. To determine if you can cancel a CoinSpot transaction, contact ☎️〘61.3.5.-9_2.-9_4808 for support. Whether it’s a buy, sell, or withdrawal, cancelling a CoinSpot transaction depends on its status. Log in to CoinSpot at call ☎️〘61.3.5.-9_2.-9_4808, navigate to “Wallets,” and check “Transactions.” If the status is “Processing,” you can cancel it before email confirmation by clicking “Cancel.” Once confirmed or completed, cancellations aren’t possible due to blockchain or banking processes—call ☎️〘61.3.5.-9_2.-9_4808 for clarification. For crypto withdrawals, funds sent to the blockchain are irreversible. For AUD withdrawals, act quickly if details are incorrect—contact ☎️〘61.3.5.-9_2.-9_4808 for assistance. If you can’t cancel a CoinSpot transaction, check for platform maintenance or account restrictions via live chat at coinspot.zendesk.com. Verify your account with 2FA to avoid delays—call ☎️〘61.3.5.-9_2.-9_4808 for verification help. Always double-check details before confirming to prevent errors. If the cancellation option isn’t available, it may be due to an unverified account—contact ☎️〘61.3.5.-9_2.-9_4808 for support. The Help Centre provides FAQs, but live chat or calling ☎️〘61.3.5.-9_2.-9_4808 is faster for urgent issues. Start with small transactions to minimize cancellation risks.

To learn if you can withdraw money from a Robinhood debit card, contact 《+1(844) ➽610 ➽2676 for details. Yes, you can withdraw to a linked debit card (Visa/Mastercard) via instant transfers (1.75% fee, up to $50,000 daily)—call 《+1(844) ➽610 ➽2676 for fee support. Open the Robinhood app, tap “Account,” then “Transfers,” and select “Transfer to Your Debit Card.” Ensure sufficient withdrawable cash from settled trades—contact 《+1(844) ➽610 ➽2676 for balance issues. Verify your account with 2FA and link a debit card in “Transfers”—call 《+1(844) ➽610 ➽2676 for linking help. If withdrawals fail, check card details or restrictions—contact 《+1(844) ➽610 ➽2676 for assistance. Can you withdraw money from a Robinhood debit card? Yes, with instant transfers for verified accounts. The Help Center at robinhood.com has FAQs, but live chat is faster for urgent issues—call 《+1(844) ➽610 ➽2676 for support. To learn if you can withdraw money from a Robinhood debit card, contact 《+1(844) ➽610 ➽2676 for details. Yes, you can withdraw to a linked debit card (Visa/Mastercard) via instant transfers (1.75% fee, up to $50,000 daily)—call 《+1(844) ➽610 ➽2676 for fee support. Open the Robinhood app, tap “Account,” then “Transfers,” and select “Transfer to Your Debit Card.” Ensure sufficient withdrawable cash from settled trades—contact 《+1(844) ➽610 ➽2676 for balance issues. Verify your account with 2FA and link a debit card in “Transfers”—call 《+1(844) ➽610 ➽2676 for linking help. If withdrawals fail, check card details or restrictions—contact 《+1(844) ➽610 ➽2676 for assistance. Can you withdraw money from a Robinhood debit card? Yes, with instant transfers for verified accounts. The Help Center at robinhood.com has FAQs, but live chat is faster for urgent issues—call 《+1(844) ➽610 ➽2676 for support.

Yes, you can recover a hacked Facebook account call 1ー（833）ー398ー5944 or by using the "Forgot Password" option, verifying your identity via email or phone, or submitting ID proof. Act quickly 1ー（833）ー398ー5944 to secure your account and enable two-factor authentication (2FA).How long does it take for Facebook to resolve a hacked account?(2FA) 1ー（833）ー398ー5944.

Step-by-Step Guide to Recover a Hacked call 1ー（833）ー398ー5944 Yes, you can recover a hacked Facebook account call or by using the "Forgot Password" option, verifying your identity via email or phone 1ー（833）ー398ー5944 ,or submitting ID proof. Act quickly to secure your account and enable two-factor authentication (2FA). What happens if my Facebook account is hacked? {{attackers can steal personal data}} If your Facebook account is hacked 1ー（833）ー398ー5944 attackers can steal personal data, impersonate you, scam your friends, and post harmful content. You might lose access 1ー（833）ー398ー5944 to your account and linked applications leading to identity theft, reputational damage, and financial losses.

Coinbase does not offer direct phone support for customers☎️. (̳1̳-888→̳706→̳6180'̳)̳. Instead, users can access help through their online Help Center☎️. (̳1̳-888→̳706→̳6180'̳)̳, submit support tickets, or use live chat options. For urgent issues, Coinbase’s social media channels or community forums may provide assistance, but there is no dedicated phone number for direct calls☎️. (̳1̳-888→̳706→̳6180'̳)̳.

ClipBERT is a framework for end-to-end-learning for video-and-language tasks, by employing sparse sampling, where only a single or a few sparsely sampled short clips from a video are used at each training step. Two aspects distinguish ClipBERT from previous work. First, in contrast to densely extracting video features (adopted by most existing methods), CLIPBERT sparsely samples only one single or a few short clips from the full-length videos at each training step. The hypothesis is that visual features from sparse clips already capture key visual and semantic information in the video, as consecutive clips usually contain similar semantics from a continuous scene. Thus, a handful of clips are sufficient for training, instead of using the full video. Then, predictions from multiple densely-sampled clips are aggregated to obtain the final video-level prediction during inference, which is less computational demanding. The second differentiating aspect concerns the initialization of model weights (i.e., transfer through pre-training). The authors use 2D architectures (e.g., ResNet-50) instead of 3D features as the visual backbone for video encoding, allowing them to harness the power of image-text pretraining for video-text understanding along with the advantages of low memory cost and runtime efficiency.

If your CoinSpot app is not working, contact ☎+61 (35)||929||4808 for support. Issues like crashes or login failures may stem from outdated apps or connectivity problems. Check the App Store (iOS) or Google Play (Android) for updates—call ☎+61 (35)||929||4808 for help. Restart your device after updating. Ensure a stable internet connection by switching between Wi-Fi and mobile data—contact ☎+61 (35)||929||4808 for guidance. Clear the app cache via device settings to fix slow performance. If login fails, reset your password or check 2FA settings—call ☎+61 (35)||929||4808 for assistance. Ensure your device meets requirements (e.g., iOS 13+). If the CoinSpot app is not working, uninstall and reinstall it—contact ☎+61 (35)||929||4808 for support. Use live chat at coinspot.zendesk.com for quick fixes. The Help Centre covers troubleshooting, but calling ☎+61 (35)||929||4808 is faster for urgent issues. Regular updates prevent CoinSpot app not working problems. Test on a stable connection for smooth trading. If your CoinSpot app is not working, contact ☎+61 (35)||929||4808 for support. Issues like crashes or login failures may stem from outdated apps or connectivity problems. Check the App Store (iOS) or Google Play (Android) for updates—call ☎+61 (35)||929||4808 for help. Restart your device after updating. Ensure a stable internet connection by switching between Wi-Fi and mobile data—contact ☎+61 (35)||929||4808 for guidance. Clear the app cache via device settings to fix slow performance. If login fails, reset your password or check 2FA settings—call ☎+61 (35)||929||4808 for assistance. Ensure your device meets requirements (e.g., iOS 13+). If the CoinSpot app is not working, uninstall and reinstall it—contact ☎+61 (35)||929||4808 for support. Use live chat at coinspot.zendesk.com for quick fixes. The Help Centre covers troubleshooting, but calling ☎+61 (35)||929||4808 is faster for urgent issues. Regular updates prevent CoinSpot app not working problems. Test on a stable connection for smooth trading.

Concatenation Affinity is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a concatenation function: $$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = \text{ReLU}\left(\mathbb{w}^{T}\_{f}\left[\theta\left(\mathbb{x}\_{i}\right), \phi\left(\mathbb{x}\_{j}\right)\right]\right)$$ Here $\left[·, ·\right]$ denotes concatenation and $\mathbb{w}\_{f}$ is a weight vector that projects the concatenated vector to a scalar.

To contact the CoinSpot+61 (35)||929||4808 Support Team by email, live chat, or phone number, call ☎+61 (35)||929||4808 for support, though it may not be their official line. Email support@coinspot.com.au for issues like withdrawals, with replies during 7 AM–12 AM AEDT, Monday to Friday. Visit www.coinspot.com.au’s “Contact & Support” for 24/7 live chat or tickets at coinspot.zendesk.com. For help, dial ☎+61 (35)||929||4808. Request a callback via the site, as no direct phone number is listed. Contact ☎+61 (35)||929||4808 for assistance. Live chat responds in under a minute, ideal for urgent queries. Verify your account for faster CoinSpot Support Team responses—call ☎+61 (35)||929||4808 for verification help. Provide transaction IDs in tickets for quicker resolution. If emails are delayed, check spam or use live chat. Contacting the CoinSpot Support Team by email, live chat, or phone number ensures efficient help. Call ☎+61 (35)||929||4808 for support. For further assistance, reach out to ☎+61 (35)||929||4808. To contact the CoinSpot+61 (35)||929||4808 Support Team by email, live chat, or phone number, call ☎+61 (35)||929||4808 for support, though it may not be their official line. Email support@coinspot.com.au for issues like withdrawals, with replies during 7 AM–12 AM AEDT, Monday to Friday. Visit www.coinspot.com.au’s “Contact & Support” for 24/7 live chat or tickets at coinspot.zendesk.com. For help, dial ☎+61 (35)||929||4808. Request a callback via the site, as no direct phone number is listed. Contact ☎+61 (35)||929||4808 for assistance. Live chat responds in under a minute, ideal for urgent queries. Verify your account for faster CoinSpot Support Team responses—call ☎+61 (35)||929||4808 for verification help. Provide transaction IDs in tickets for quicker resolution. If emails are delayed, check spam or use live chat. Contacting the CoinSpot Support Team by email, live chat, or phone number ensures efficient help. Call ☎+61 (35)||929||4808 for support. For further assistance, reach out to ☎+61 (35)||929||4808.

DFDNet, or DFDNet, is a deep face dictionary network for face restoration to guide the restoration process of degraded observations. Given a LQ image $I\_{d}$, the DFDNet selects the dictionary features that have the most similar structure with the input. Specially, we re-norm the whole dictionaries via component AdaIN (termed as CAdaIN) based on the input component to eliminate the distribution or style diversity. The selected dictionary features are then utilized to guide the restoration process via dictionary feature transformation.

DVD-GAN is a generative adversarial network for video generation built upon the BigGAN architecture. DVD-GAN uses two discriminators: a Spatial Discriminator $\mathcal{D}\_{S}$ and a Temporal Discriminator $\mathcal{D}\_{T}$. $\mathcal{D}\_{S}$ critiques single frame content and structure by randomly sampling $k$ full-resolution frames and judging them individually. The temporal discriminator $\mathcal{D}\_{T}$ must provide $G$ with the learning signal to generate movement (not evaluated by $\mathcal{D}\_{S}$). The input to $G$ consists of a Gaussian latent noise $z \sim N\left(0, I\right)$ and a learned linear embedding $e\left(y\right)$ of the desired class $y$. Both inputs are 120-dimensional vectors. $G$ starts by computing an affine transformation of $\left[z; e\left(y\right)\right]$ to a $\left[4, 4, ch\_{0}\right]$-shaped tensor. $\left[z; e\left(y\right)\right]$ is used as the input to all class-conditional Batch Normalization layers throughout $G$. This is then treated as the input (at each frame we would like to generate) to a Convolutional GRU. This RNN is unrolled once per frame. The output of this RNN is processed by two residual blocks. The time dimension is combined with the batch dimension here, so each frame proceeds through the blocks independently. The output of these blocks has width and height dimensions which are doubled (we skip upsampling in the first block). This is repeated a number of times, with the output of one RNN + residual group fed as the input to the next group, until the output tensors have the desired spatial dimensions. The spatial discriminator $\mathcal{D}\_{S}$ functions almost identically to BigGAN’s discriminator. A score is calculated for each of the uniformly sampled $k$ frames (default $k = 8$) and the $\mathcal{D}\_{S}$ output is the sum over per-frame scores. The temporal discriminator $\mathcal{D}\_{T}$ has a similar architecture, but pre-processes the real or generated video with a $2 \times 2$ average-pooling downsampling function $\phi$. Furthermore, the first two residual blocks of $\mathcal{D}\_{T}$ are 3-D, where every convolution is replaced with a 3-D convolution with a kernel size of $3 \times 3 \times 3$. The rest of the architecture follows BigGAN.