Weevaluated all models onthree additional tasks, beyond those presented inthe main paper. Point-of-no-return (PNR) temporal localization error:Given a video clip of a state change, the networkhastoestimate thetimeatwhich astatechange begins. More specifically,themodel tries toestimate the keyframe within the video clip that contains the point-of-no-return (the time when the state change begins). The occurrence ofstate change isthen predicted bytraining abinary linear classifier, using the concatenated representations as input. ActionRecognition(AR)w/audio:Forthistask,videoembeddings fromfV andaudioembedding from fA are concatenated together and passed through two separate linear classifiers to classify the'verb' and'noun' of the action occurring in the video clip.

Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific features of the area, such as size and shape, have a relevant impact on the possibility of implementing the digital logic with the required functionality. We present a Machine Learning (ML)-based evaluation methodology for predicting the feasibility of digital implementation using a set of high-level features. This approach aims to avoid time-consuming Place-and-Route trials, enabling rapid feedback between Digital and Analog Back-End designers during top-level placement.

Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks, to synergistically exploit them when learning novel skills. To accomplish this, we seek for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead, to support multiple downstream tasks and enable cooperation when learning novel skills. We then propose EgoPack, a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights, as a backpack of skills that a robot can carry around and use when needed. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks, outperforming current state-of-the-art methods.

Neural Implicit Representations (NIR) have gained significant attention recently due to their ability to represent complex and high-dimensional data. Unlike explicit representations, which require storing and manipulating individual data points, implicit representations capture information through a learned mapping function without explicitly representing the data points themselves. They often prune or quantize neural networks after training to accelerate encoding/decoding speed, yet we find that conventional methods fail to transfer learned representations to new videos. This work studies the continuous expansion of implicit video representations as videos arrive sequentially over time, where the model can only access the videos from the current session. We propose a novel neural video representation, Progressive Neural Representation (PNR), that finds an adaptive substructure from the supernet for a given video based on Lottery Ticket Hypothesis. At each training session, our PNR transfers the learned knowledge of the previously obtained subnetworks to learn the representation of the current video while keeping the past subnetwork weights intact. Therefore it can almost perfectly preserve the decoding ability (i.e., catastrophic forgetting) of the NIR on previous videos. We demonstrate the effectiveness of our proposed PNR on the neural sequential video representation compilation on the novel UVG8/17 video sequence benchmarks. The public code is available at https://github.com/ihaeyong/PNR.

While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time.In this paper, we introduce regularization frequency (i.e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method. For various regularization techniques, such as weight decay and dropout, optimizing the regularization strength is crucial to improve generalization in Deep Neural Networks (DNNs). While model compression also demands the right amount of regularization, the regularization strength incurred by model compression has been controlled only by compression ratio. Throughout various experiments, we show that regularization frequency critically affects the regularization strength of model compression. Combining regularization frequency and compression ratio, the amount of weight updates by model compression per mini-batch can be optimized to achieve the best model accuracy. Modulating regularization frequency is implemented by occasional model compression while conventional compression-aware training is usually performed for every mini-batch.