12/19/2023 0 Comments Qbserve tracking video![]() Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the researchĬommunity. ![]() Rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where The approach to collection is designed to uphold It offers 3,025 hours of daily-lifeĪctivity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 uniqueĬamera wearers from 74 worldwide locations and 9 different countries. We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. Generalizes to image input, which can be interpreted as a video with a single frame and obtains competitive results on Supervision, MaskFeat pre-trained on unlabeled videos achieves unprecedented results of 86.7% with MViT-L on Learn abundant visual knowledge and drive large-scale Transformer-based models. Is essential for good results, which is in line with earlier work using HOG for visual recognition. We observe that the local contrast normalization in HOG Particularly well in terms of both performance and efficiency. We study fiveĭifferent types of features and find Histograms of Oriented Gradients (HOG), a hand-crafted feature descriptor, works Randomly masks out a portion of the input sequence and then predicts the feature of the masked regions. We present Masked Feature Prediction (MaskFeat) for self-supervised pre-training of video models. Our study suggests that the general framework of maskedĪutoencoding (BERT, MAE, etc.) can be a unified methodology for representation learning with minimal domain knowledge. Results of training on real-world, uncurated Instagram data. We observe that MAE can outperform supervised pre-training by large margins. We report competitive results on several challenging video datasets using vanilla Vision A high masking ratio leads to a large speedup, e.g., > 4x in Is related to information redundancy of the data. 75% on images), supporting the hypothesis that this ratio Observe that the optimal masking ratio is as high as 90% (vs. Spacetime (only except for patch and positional embeddings), and spacetime-agnostic random masking performs the best. Interestingly, we show that our MAE method can learn strong representations with almost no inductive bias on We randomly mask out spacetime patches in videos and learn an autoencoder to reconstruct them in This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to spatiotemporal representation
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