Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs. How do we achieve forward transfer at zero cost for the single SCDs? We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method that consolidates the stream of SC models without using the original data. DAC performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in reharsal-free and distributed CL scenarios. Somewhat surprisingly, even a single out-of-distribution image is sufficient as the only source of data during consolidation.

We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training set from a large-scale data pool such that a competitive model can be obtained. We propose a search and pruning (SnP) solution to this training data search problem, tailored to object re-identification (re-ID), an application aiming to match the same object captured by different cameras. Specifically, the search stage identifies and merges clusters of source identities which exhibit similar distributions with the target domain. The second stage, subject to a budget, then selects identities and their images from the Stage I output, to control the size of the resulting training set for efficient training. The two steps provide us with training sets 80\% smaller than the source pool while achieving a similar or even higher re-ID accuracy. These training sets are also shown to be superior to a few existing search methods such as random sampling and greedy sampling under the same budget on training data size. If we release the budget, training sets resulting from the first stage alone allow even higher re-ID accuracy. We provide interesting discussions on the specificity of our method to the re-ID problem and particularly its role in bridging the re-ID domain gap. The code is available at https://github.com/yorkeyao/SnP.

Industrial arms need to evolve beyond their standard shape to embrace new and emerging technologies. In this paper, we shall first perform an analysis of four popular but different modern industrial robot arms. By seeing the common trends we will try to extrapolate and expand these trends for the future. Here, particular focus will be on interaction based on augmented reality (AR) through head-mounted displays (HMD), but also through smartphones. Long-term human-robot interaction and personalization of said interaction will also be considered. The use of AR in human-robot interaction has proven to enhance communication and information exchange. A basic addition to industrial arm design would be the integration of QR markers on the robot, both for accessing information and adding tracking capabilities to more easily display AR overlays. In a recent example of information access, Mercedes Benz added QR markers on their cars to help rescue workers estimate the best places to cut and evacuate people after car crashes. One has also to deal with safety in an environment that will be more and more about collaboration. The QR markers can therefore be combined with RF-based ranging modules, developed in the EU-project SafeLog, that can be used both for safety as well as for tracking of human positions while in close proximity interactions with the industrial arms. The industrial arms of the future should also be intuitive to program and interact with. This would be achieved through AR and head mounted displays as well as the already mentioned RF-based person tracking. Finally, a more personalized interaction between the robots and humans can be achieved through life-long learning AI and disembodied, personalized agents. We propose a design that not only exists in the physical world, but also partly in the digital world of mixed reality.

There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summaries. Such an end-to-end setup would allow for many interesting applications, e.g., a text-based search that finds a relevant video from a video corpus, extracts the most relevant moment from that video, and segments the moment into important steps with captions. To address this, we present the HiREST (HIerarchical REtrieval and STep-captioning) dataset and propose a new benchmark that covers hierarchical information retrieval and visual/textual stepwise summarization from an instructional video corpus. HiREST consists of 3.4K text-video pairs from an instructional video dataset, where 1.1K videos have annotations of moment spans relevant to text query and breakdown of each moment into key instruction steps with caption and timestamps (totaling 8.6K step captions). Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks. In moment segmentation, models break down a video moment into instruction steps and identify start-end boundaries. In step captioning, models generate a textual summary for each step. We also present starting point task-specific and end-to-end joint baseline models for our new benchmark. While the baseline models show some promising results, there still exists large room for future improvement by the community. Project website: https://hirest-cvpr2023.github.io

Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher annotation cost and dimensionality. Besides, any video understanding task requires reasoning over both spatial and temporal dimensions. In order to learn both the static and motion related features for the semi-supervised action recognition task, existing methods rely on hard input inductive biases like using two-modalities (RGB and Optical-flow) or two-stream of different playback rates. Instead of utilizing unlabeled videos through diverse input streams, we rely on self-supervised video representations, particularly, we utilize temporally-invariant and temporally-distinctive representations. We observe that these representations complement each other depending on the nature of the action. Based on this observation, we propose a student-teacher semi-supervised learning framework, TimeBalance, where we distill the knowledge from a temporally-invariant and a temporally-distinctive teacher. Depending on the nature of the unlabeled video, we dynamically combine the knowledge of these two teachers based on a novel temporal similarity-based reweighting scheme. Our method achieves state-of-the-art performance on three action recognition benchmarks: UCF101, HMDB51, and Kinetics400. Code: https://github.com/DAVEISHAN/TimeBalance

The availability of vast data sources and advanced machine learning technologies has given rise to a new system of influence known as influence engineering. It can guide user behavior and lead to new customer acquisition. Using computer vision and pattern analysis techniques, companies can now recognize user emotions using emotion detection techniques (generally called emotion AI) to direct their decision-making process. Also, the advancements in emotion detection and natural language processing techniques present a significant opportunity to automate influential aspects of consumer communication and digital marketing. In fact, in 2021, Gartner named influence engineering as one of the six emerging technologies expected to drive growth for digital marketing.

Last year's release of ChatGPT alerted many to the great strides that machine learning has made, and will continue tomake in the years going forward. But how do we make surethat this great power is being used responsibly, free from bias and malicious intent? For Amazon Web Services, Diya Wynn is the senior practice manager for Responsible AI. Recently, she sat down with the New Stack to discuss all things Responsible AI. At AWS, Wynn created the customer facing responsible AI practice, and built a team of individuals with diverse backgrounds, including members of the LGBTQIA and differently-abled communities.

Over these past couple of months, ChatGPT has been all over the news. Many businesses are already leveraging the technology to get ahead of the competition. The Complete ChatGPT Artificial Intelligence OpenAI Training Bundle helps you follow suit, with four courses that showcase the hidden power of this AI platform. The training is worth a total of $800, but you can grab all four courses today for only $19.97 in a special price drop at TechRepublic Academy. Although ChatGPT has only just exploded onto the scene, the technology has had a massive impact.

As companies plunge into the world of data, skilled individuals who can extract valuable insights from an ocean of information are in high demand. Join the data revolution and secure a competitive edge for businesses vying for supremacy. Data Scientists and Analysts use various tools such as machine learning algorithms, statistical modeling, natural language processing (NLP), and predictive analytics to identify trends, uncover opportunities for improvement, and make better decisions. With the right combination of technical know-how, communication skills, problem solving abilities, and creative thinking – these professionals can help organizations gain a competitive advantage by leveraging data effectively. Data science and data analysis have rapidly emerged as flourishing and versatile career paths, encompassing a wide range of industries and applications.

Every machine learning program has a learning process that is at least partially influenced by the learning behavior and learning style of humans. An image classification model, for example, tells us the class of an image like a person using the knowledge that humans have provided in the form of data. Another type of machine learning is curriculum learning. It trains the model so that humans can learn from their education system. This article will discuss curriculum learning.