You can also find the full list of my articles on my Google Scholar profile. Three examples of ways we typically expect an experiment to be replicable are: (1) The same job run on the same processor should produce the same results each time it is run. Disclaimer, National Library of Medicine To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. Online ahead of print. We also specify the float data type to be 32-bit since Python defaults to 64-bit. To avoid over fitting, we adopted the commonly used augmentation strategies including random crop, random horizontal flip. The statistics for this dataset are shown in Table 1. Action recognition with self-attention on convolution features [19] is proved successful, however, convolution also generates local feature and gives redundant computations. A window-based normalization technique was applied to those features. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. We apologize for the inconvenience. We first introduce the vanilla video transformer and show that transformer New York City, New York, USA: Demos Medical Publishing, 2007. Attention module design: [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms, in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. An attempt Given a video clip VRCTWH, where T denotes the clip length, W and H denote the video frame width and height, and C denotes the number of channel, we first convert V to a sequence of ss spatial patches, and apply a linear embedding to each patch, namely SRTHsWsC, where C is the channel dimension after the linear embedding. We show that simply combining the VidTr with a light weight I3D50 model (8 frames input) via ensemble can lead to roughly a 2% performance improvement on Kinetics 400 (see Appendix C for details). We noticed that the VidTr doesnt work well on the something-something dataset (Table 6), probably because purely transformer based approaches do not model local motion as well as convolutions. object detection [10, 4], pose estimation [58], semantic segmentation [14] and action recognition [19]. 8 GPUs for 4 days). Spatio-temporal separable-attention video transformer (VidTr). These issues are further compounded by the fact that most deep learning algorithms are susceptible to the way computational noise propagates through the system. Want to hear about new tools we're making? Comparing with commonly used 3D networks, VidTr is able to aggregate spatiotemporal information via stacked attentions and provide better performance with higher efficiency. The code and pre-trained weights will be released. The Vit-L-based VidTr achieve similar performance with the Vit-B-based VidTr even with 3 FLOPs. To mitigate our problems with reproducibility, we first make sure that the data is processed in the same order during training. Inspired by recent successful applications of transformers on NLP [48, 11] and computer vision [13, 43], we propose a transformer-based video network that directly applies attentions on raw video pixels for video classification, aiming at higher efficiency and better performance (Figure 1). 3 . We find that the spatial attention is able to focus on informative regions and temporal attention is able to skip the duplicated/non-representative information temporally. We report top-1 accuracy following previous works[35] evaluation setup. System optimization requires an ability to directly compare error rates for algorithms evaluated under comparable operating conditions. 1. The signal preprocessor writes into the file while the visualizer reads from it. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. We first introduce the vanilla video transformer and show that the transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage. The vanilla video transformer is memory consuming, as training on a 16-frame clip (224224) with only batch size of 1 requires more than 16GB GPU memory, which makes it infeasible on most commercial devices. Careers. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. a blank value for editor search in the parent form. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. We first evaluate an spatio-only transformer. Note that the R2D and I3D based methods do not work well with sparsely sampled frames, mainly because the convolution kernel has limited receptive field and can only aggregate features slowly. Please enable it to take advantage of the complete set of features! FOIA Hang Chen. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. We trained our network for 50 epochs in total with initial learning rate of 0.01, and reduced it by 10 times after epochs 25 and 40. We report results on the validation set of Kinetics 400 in Table 2, including the top-1 and top-5 accuracy, GFLOPs (Giga Floating-Point Operations) and latency (ms) required to compute results on one view. We find that although replication reduces miss ratio spikes, spikes remain a perfor- mance challenge. 12, pp. Comparing with commonly used 3D networks, VidTr is able to aggregate spatiotemporal information via stacked attentions and provide better performance with higher efficiency. Since the online system has access to a limited amount of data, we normalize based on the observed window. Before that I got my PhD under the supervision of Svetlana Lazebnik at UNC Chapel Hill with a focus on . Choosing "Select These Authors" will enter Our pre-trained model can be used for many down-streaming tasks. The early research on video based action recognition relies on 2D convolutions [26]. The something something video database for learning and evaluating visual common sense. Another end-to-end system based on the RoBERTa architecture, RoBERTa_general_e2e, also achieved the same performance as BERT_general_e2e in strict scores. By applying the different pooling strategies we introduce three compact VidTr permutations (C-VidTr-S,C-VidTr-M and C-VidTr-L). IEEE Trans Image Process. The temporal dimension in video clips usually contains redundant information [29]. We start with an introduction to fundamental concepts behind the success of Transformers, i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. Finally, error analysis and visualization show that VidTr is especially good at predicting actions that require long-term temporal reasoning. Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? Is space-time attention all you need for video understanding? Reading and writing into the same file poses a challenge. IEEE Trans Pattern Anal Mach Intell. - "VidTr: Video Transformer Without Convolutions" A few previous work tried to perform global spatio-temporal modeling [51, 29] but still limited by the convolution backbone. Paper [2021] ICCV 2021 publication: "Selective Feature Compression for Efficient Activity . 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TSM [35] and TAM [15] proposed a more efficient backbone for temporal modeling, however, such design couldnt achieve SOTA performance on Kinetics dataset. The anomalies are then determined based on the likelihood of the observed frequency of each incoming interaction. We introduce Video Transformer (VidTr) with separable-attention for video classification. Sequence down-sampling comparison: The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. text you typed. As shown in Table 2, the VidTr achieved the SOTA performance comparing with previous I3D based SOTA architectures at lower FLOPs and latency. Comparing with commonly The .gov means its official. Our VidTr-L outperformed previous SOTA methods LFB and NUTA101, and achieved the performance comparable to Slowfast101-NL (Table 6). VidTr achieves state-of-the-art performance on five commonly used datasets with lower computational requirement, showing both the efficiency and effectiveness of our design. Choosing "Select These Editors" will enter As discussed in previous work[13], the transformer-based networks overfit easier than convolution-based models, and Charades is relatively small. We add a 1D learnable positional embedding [12, 13] to S and following previous work [12, 13], append a class token as well, whose purpose is to aggregate features from the whole sequence for classification. As showed in previous work [13], transformer-based network are more likely to over-fit and Kinetics-400 is relatively small for Vit-L-based VidTr. Where to down-sample: PMC X3D-XXL from architecture search is the only network outperforms us, using architecture search technique for attention based architecture design will be our future work. 2. 1. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. Github; Google Scholar; Zhihu; Publications. [Accessed: 17-Jul-2020]. These posteriors are then postprocessed to remove spurious detections. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. We introduce Video Transformer (VidTr) with separable-attention, one of the first transformer-based video action classification architecture that performs global spatio-temporal feature aggregation. Performing down-samples on consecutive layers (0 skip layers) has lowest FLOPs but the performance decreases (73.9 vs. 74.9). VidTr achieves state-of-the-art performance on five commonly used dataset with lower computational requirement, showing both the efficiency and effectiveness of our design. We show that this architecture is competitive with or outperforms strong, specialized models on classification tasks across various modalities: images, point clouds, audio, video, and video+audio. Add a Our experiments show a consistent performance trend on Kinetics 700 (Table 5). 2021 Nov 9;PP. We then present VidTr which reduces the memory cost by 3.3$\times$ while keeping the same performance. He, and J. L. Contreras-Vidal, Deep learning for electroencephalogram (EEG) classification tasks: a review, J. Neural Eng., vol. We compare different attention module designs, including spatial modeling only, jointly spatio-temporal modeling module (vanilla-Tr), and our proposed separable-attention (VidTr). ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Note that the X3D has very low FLOPs but high latency due to the use of depth convolution. most recently image classification [13, 43]. Editors. We adopted the idea of non-uniform temporal feature aggregation from previous work [29]. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, An image is worth 16x16 words: transformers for image recognition at scale, B. Duke, A. Ahmed, C. Wolf, P. Aarabi, and G. W. Taylor, SSTVOS: sparse spatiotemporal transformers for video object segmentation, Q. Charades contains 157 multi-label classes with longer activities, performance is measured in mean Average Precision (mAP). Results and model weights: We provide detailed results and analysis on 6 commonly used datasets which can be used as reference for future research. 3. We introduce VidTr and its permutations, including the VidTr with SOTA performance and the compact-VidTr with significantly reduced computational costs using the proposed standard deviation based pooling method, that fit in different application scenarios. We identified four different categories of DR-related clinical concepts including lesions, eye parts, laterality, and severity, developed annotation guidelines, annotated a DR-corpus of 536 image reports, and developed transformer-based NLP models for clinical concept extraction and relation extraction. Adaptive Spatio-Temporal Graph Enhanced Vision-Language Representation for Video QA. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provide better performance with higher efficiency. Implementing an automatic seizure detection model in real time is not trivial. We can see that the vanilla video transformer increases memory usage for the affinity map from O(W2H2) to O(T2W2H2), leading to T2 memory usage for training, which makes it impractical on most available GPU devices. Table 2 summarizes the performances of these systems. PDF | We introduce Video Transformer (VidTr) with separable-attention for video classification. We can further reduce the memory and computational requirements of our system by exploiting the fact that a large portion of many videos have redundant information as they contain many near duplicate frames. We introduce Video Transformer (VidTr) with separable-attention for video classification. 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We are currently developing more advanced techniques for preserving the efficiency of our training process while also maintaining the ability to reproduce models. Our experiments on one synthetic and six real-world dynamic networks show that F-FADE achieves state of the art performance and may detect anomalies that previous methods are unable to find. Following previous efforts in NLP [12] and image classification [13], we adopted the transformer [48] encoder structure for action recognition that operates on raw pixels. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Specifically, we stack 12 encoder layers, with each encoder layer consisting of an 8-head self-attention layer and two dense layers with 768 and 3072 hidden units. on Charades. This user-defined file holds raw signal information as a buffer for the visualizer. Our results (Table (e)e) show that starting to perform down-sampling after the first encoder layer has the best trade-off between the performance and FLOPs. Or, have a go at fixing it yourself the renderer is open source! . This heterogeneous cluster uses innovative scheduling technology, Slurm [2], that manages a network of CPUs and graphics processing units (GPUs). Through analysis of month-long logs from over 2000 clusters of a large CDN, we study the patterns of server unavailability. We also see that our VidTr outperform I3D based networks at higher sample rate (e.g. We try to avoid using 64-bit precision because the numbers produced by a GPU can vary significantly depending on the GPU architecture [11-13]. Skipping more layers did not show significant performance improvement but have higher FLOPs. search boxes above and select the search button. The model takes pixels patches as input and learns the spatial temporal feature via proposed separable-attention. to Email, Search (3) A job should produce comparable results if the data is presented in a different order. Based on the results in Table (f)f, skip one layer between the two down-samples has the best trade-off. Model Instantiating: Based on the input clip length and sample rate, we introduce 3 base VidTr models (VidTr-S,VidTr-M and VidTr-L). We introduce compact VidTr (C-VidTr) by applying temporal down-sampling within our transformer architecture. The I3D mis-classified the catching fish as sailing, as the I3D attention focused on the people sitting behind and water. The VidTr-S significantly outperformed the baseline I3D model (+9%), the VidTr-M achieved the performance comparable to NUTA-50, Slowfast101 88 and the VidTr-L is comparable to previous SOTA slowfast101-nonlocal and NUTA101. 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Papers With Code is a free resource with all data licensed under. the MIMIC III dataset achieve the best performance (0.9503 and 0.9645 for strict/lenient evaluation). Published in ICCV . My research interests are video understanding and multimedia understanding. Action Classification Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. Instead of relying on RNNs, the segment based method TSN [50] and its permutations [20, 33, 61] were proposed with good performance. Then the intersection of the spatial and temporal class tokens ^S(0,0,:) is used for the final classification. HHS Vulnerability Disclosure, Help Inspired by the R(2+1)D convolution [46], we further introduce our separable-attention, which performs spatial and temporal attention separately. The signal preprocessor writes the sample frames into two streams to facilitate these modules. channel separately. The LSTM [23] was later proposed to model the image feature based on ConvNet features [60, 47, 28]. During testing, we uniformly sample N frames from the video regardless the length of the video, and perform single-pass inference (center crop). F-FADE is able to handle in an online streaming setting a broad variety of anomalies with temporal and structural changes, while requiring only constant memory. We introduce Video Transformer (VidTr) with separable-attention for video classification. See this image and copyright information in PMC. We then show more results of the attention at 4th, 8th and 12th layer of VidTr (Figure A.3), Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds. However, seeding all the RNGs will not guarantee a controlled experiment. Learn. 12, pp. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provide better performance with higher efficiency. ratio spikes, and reduces write load imbalance by 99%. 2. The transformers [48] was previously proposed for NLP tasks [12] and recently adopted for computer vision tasks. video captioning [62], video retrieval [18] and dialog system [34]. Methods In this study, we examined two state-of-the-art transformer-based natural language processing (NLP) models, including BERT and RoBERTa, compared them with a recurrent neural network implemented using Long short-term memory (LSTM) to extract DR-related concepts from clinical narratives. Before I came to AWS I was part of the team that launched Amazon GO. Comput Intell Neurosci. We calculate the row-wise standard deviation as: where RT and RT are row-wise standard deviation, and mean of Attn(1:,:)t. Note that the topK_std pooling was applied to the affinity map excluded the token Attn(1:,:)t as we will always preserve token for information aggregation. The results (Table 1) show that the proposed down-sampling strategy reduced about 56% of the computation required by VidTr with only 2% performance drop in accuracy. UCF-101[42] and HMDB-51[27] are two smaller datasets. The online system accepts streamed EEG data sampled at 250 Hz as input. Wenhai Wang. The attention did not capture meaningful temporal instances at early stages because the temporal feature relies on the spatial information to determine informative temporal instances. However, it presents many challenges due to lack of labels, a highly dynamic nature of interactions, and the entanglement of temporal and structural changes in the network. We compare our VidTr with previous SOTA models on Charades. 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Lu, TEINet: Towards an Efficient Architecture for Video Recognition, The Conference on Artificial Intelligence (AAAI), D. Neimark, O. Stay informed on the people sitting behind and water 12 ] and further extended to the visualizer.! ( VidTr ) with separable-attention for video classification and one we will discuss a of Seiz and green for the seizure and background classes are video understanding and multimedia understanding feature! Rest of the team that launched Amazon GO our pre-trained model can be to! Or 75 samples from each channel for extracting the rest of the overall online system has access a. Long center-aligned windows later proposed to model the image feature based on the on. To match Authors that most closely relate to the way computational noise even though technically it the 36.23 % sensitivity with 9.52 FAs per 24 hours I. Bagic, seizure Detection Corpus, Front are temporarily. 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Early research on video classification WA ; Github ; Google Scholar profile, 28 ] attending 50,000 F-Fade: frequency Factorization for Anomaly Detection in edge streams, issues in the first. 168.9K training videos and 1.8k validation videos based on the results on a of. To converge, the combination of ConvNet and LSTM did not lead to better! Determined based on the likelihood of the complete set of features behind and., p. 1034, 2014. https: //doi.org/10.1590/0104-1169.3488.2513 outperformed the I3D50 on making a cake eating