Keyur Muzumdar

Matthew Walmer, Rose Kanjirathinkal, Kai Sheng Tai et al.

The area of temporally fine-grained video representation learning focuses on generating frame-by-frame representations for temporally dense tasks, such as fine-grained action phase classification and frame retrieval. In this work, we advance the state-of-the-art for self-supervised models in this area by re-examining the design of transformer architectures for video representation learning. A key aspect of our approach is the improved sharing of scene information in the temporal pipeline by representing multiple salient entities per frame. Prior works use late-fusion architectures that reduce frames to a single-dimensional vector before modeling any cross-frame dynamics. In contrast, our Multi-entity Video Transformer (MV-Former) processes the frames as groups of entities represented as tokens linked across time. To achieve this, we propose a Learnable Spatial Token Pooling strategy to identify and extract features for multiple salient regions per frame. Through our experiments, we show that MV-Former outperforms previous self-supervised methods, and also surpasses some prior works that use additional supervision or training data. When combined with additional pre-training data from Kinetics-400, MV-Former achieves a further performance boost. Overall, our MV-Former achieves state-of-the-art results on multiple fine-grained video benchmarks and shows that parsing video scenes as collections of entities can enhance performance in video tasks.

Fred Lin, Keyur Muzumdar, Nikolay Pavlovich Laptev et al.

Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs jointly for understanding production issues. Another challenge in reviewing the logs for identifying issues is the scale - there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability. We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling large-scale production datasets. We have successfully rolled out this approach for root cause investigation purposes in a large-scale infrastructure. We also present the setup and results from multiple production use cases in this paper.