Transformer architecture is a defacto standard for modeling global dependency in long sequences. However, quadratic space and time complexity for self-attention prohibits transformers from scaling to extremely long sequences (> 10k). Low-rank decomposition as a non-negative matrix factorization (NMF) of self-attention demonstrates remarkable performance in linear space and time complexity with strong theoretical guarantees. However, our analysis reveals that NMF-based works struggle to capture the rich spatio-temporal visual cues scattered across the long sequences resulting from egocentric lifelogs. To capture such cues, we propose a novel attention mechanism named SEMantic Atention (SEMA), which factorizes the self-attention matrix into a semantically meaningful subspace. We demonstrate SEMA in a representation learning setting, aiming to recover activity patterns in extremely long (weeks-long) egocentric lifelogs using a novel self-supervised training pipeline. Compared to the current state-of-the-art, we report significant improvement in terms of (NMI, AMI, and F-Score) for EgoRoutine, UTE, and Epic Kitchens datasets. Furthermore, to underscore the efficacy of SEMA, we extend its application to conventional video tasks such as online action detection, video recognition, and action localization. Code is available at https://github.com/Pravin74/Semantic_attention/

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day long first-person videos. The highly redundant nature of these videos and extreme camera-shakes make them difficult to watch from beginning to end. These videos require efficient summarization tools for consumption. However, traditional summarization techniques developed for static surveillance videos or highly curated sports videos and movies are either not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. This paper presents a novel unsupervised reinforcement learning framework to summarize egocentric videos both in terms of length and the content. The proposed framework facilitates incorporating various prior preferences such as faces, places, or scene diversity and interactive user choice in terms of including or excluding the particular type of content. This approach can also be adapted to generate summaries of various lengths, making it possible to view even 1-minute summaries of one's entire day. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). The quantitative comparisons on the benchmark Disney dataset show that our method achieves significant improvement in Relaxed F-Score (RFS) (29.60 compared to 19.21 from SOTA), BLEU score (0.68 compared to 0.67 from SOTA), Average Human Ranking (AHR), and unique events covered. Finally, we show that our technique can be applied to summarize traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 46.40 and 57.6 to 58.3 respectively. We also provide a Pytorch implementation and a web demo at https://pravin74.github.io/Int-sum/index.html.

The long and unconstrained nature of egocentric videos makes it imperative to use temporal segmentation as an important pre-processing step for many higher-level inference tasks. Activities of the wearer in an egocentric video typically span over hours and are often separated by slow, gradual changes. Furthermore, the change of camera viewpoint due to the wearer's head motion causes frequent and extreme, but, spurious scene changes. The continuous nature of boundaries makes it difficult to apply traditional Markov Random Field (MRF) pipelines relying on temporal discontinuity, whereas deep Long Short Term Memory (LSTM) networks gather context only upto a few hundred frames, rendering them ineffective for egocentric videos. In this paper, we present a novel unsupervised temporal segmentation technique especially suited for day-long egocentric videos. We formulate the problem as detecting concept drift in a time-varying, non i.i.d. sequence of frames. Statistically bounded thresholds are calculated to detect concept drift between two temporally adjacent multivariate data segments with different underlying distributions while establishing guarantees on false positives. Since the derived threshold indicates confidence in the prediction, it can also be used to control the granularity of the output segmentation. Using our technique, we report significantly improved state of the art f-measure for daylong egocentric video datasets, as well as photostream datasets derived from them: HUJI~(73.01%, 59.44%), UTEgo~(58.41%, 60.61%) and Disney~(67.63%, 68.83%).

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day-long first-person videos. Because of the extreme shake and highly redundant nature, these videos are difficult to watch from beginning to end and often require summarization tools for their efficient consumption. However, traditional summarization techniques developed for static surveillance videos, or highly curated sports videos and movies are, either, not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. In this paper, we present a novel unsupervised reinforcement learning technique to generate video summaries from day long egocentric videos. Our approach can be adapted to generate summaries of various lengths making it possible to view even 1-minute summaries of one's entire day. The technique can also be adapted to various rewards, such as distinctiveness and indicativeness of the summary. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). Quantitative comparison on the benchmark Disney dataset shows that our method achieves significant improvement in Relaxed F-Score (RFS) (32.56 vs. 19.21) and BLEU score (12.12 vs. 10.64). Finally, we show that our technique can be applied for summarizing traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 45.6 and 57.6 to 59.1 respectively.

Automated brain tissue segmentation into white matter (WM), gray matter (GM), and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple sclerosis, etc. However, thin GM structures at the periphery of cortex and smooth transitions on tissue boundaries such as between GM and WM, or WM and CSF pose difficulty in building a reliable segmentation tool. This paper proposes a Fully Convolutional Neural Network (FCN) tool, that is a hybrid of two widely used deep learning segmentation architectures SegNet and U-Net, for improved brain tissue segmentation. We propose a skip connection inspired from U-Net, in the SegNet architetcure, to incorporate fine multiscale information for better tissue boundary identification. We show that the proposed U-SegNet architecture, improves segmentation performance, as measured by average dice ratio, to 89.74% on the widely used IBSR dataset consisting of T-1 weighted MRI volumes of 18 subjects.

We focus on first-person action recognition from egocentric videos. Unlike third person domain, researchers have divided first-person actions into two categories: involving hand-object interactions and the ones without, and developed separate techniques for the two action categories. Further, it has been argued that traditional cues used for third person action recognition do not suffice, and egocentric specific features, such as head motion and handled objects have been used for such actions. Unlike the state-of-the-art approaches, we show that a regular two stream Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) architecture, having separate streams for objects and motion, can generalize to all categories of first-person actions. The proposed approach unifies the feature learned by all action categories, making the proposed architecture much more practical. In an important observation, we note that the size of the objects visible in the egocentric videos is much smaller. We show that the performance of the proposed model improves after cropping and resizing frames to make the size of objects comparable to the size of ImageNet's objects. Our experiments on the standard datasets: GTEA, EGTEA Gaze+, HUJI, ADL, UTE, and Kitchen, proves that our model significantly outperforms various state-of-the-art techniques.