Diffusion models, widely recognized for their success in generative tasks, have not yet been applied to clustering. We introduce Clustering via Diffusion (CLUDI), a self-supervised framework that combines the generative power of diffusion models with pre-trained Vision Transformer features to achieve robust and accurate clustering. CLUDI is trained via a teacher-student paradigm: the teacher uses stochastic diffusion-based sampling to produce diverse cluster assignments, which the student refines into stable predictions. This stochasticity acts as a novel data augmentation strategy, enabling CLUDI to uncover intricate structures in high-dimensional data. Extensive evaluations on challenging datasets demonstrate that CLUDI achieves state-of-the-art performance in unsupervised classification, setting new benchmarks in clustering robustness and adaptability to complex data distributions. Our code is available at https://github.com/BGU-CS-VIL/CLUDI.

We present a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments. We elucidate how individual components regarding image clustering impact image synthesis across three datasets. By combining recent advancements from image clustering and diffusion models, we show that, given the optimal cluster granularity with respect to image synthesis (visual groups), cluster-conditioning can achieve state-of-the-art FID (i.e. 1.67, 2.17 on CIFAR10 and CIFAR100 respectively), while attaining a strong training sample efficiency. Finally, we propose a novel method to derive an upper cluster bound that reduces the search space of the visual groups using solely feature-based clustering. Unlike existing approaches, we find no significant connection between clustering and cluster-conditional image generation. The code and cluster assignments will be released.

We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. Our approach involves self-distillation training of clustering heads based on the fact that nearest neighbours in the pretrained feature space are likely to share the same label. We propose a novel objective that learns associations between image features by introducing a variant of pointwise mutual information together with instance weighting. We demonstrate that the proposed objective is able to attenuate the effect of false positive pairs while efficiently exploiting the structure in the pretrained feature space. As a result, we improve the clustering accuracy over $k$-means on $17$ different pretrained models by $6.1$\% and $12.2$\% on ImageNet and CIFAR100, respectively. Finally, using self-supervised vision transformers, we achieve a clustering accuracy of $61.6$\% on ImageNet. The code is available at https://github.com/HHU-MMBS/TEMI-official-BMVC2023.

In a bid to run with the tide of digital economy, fresh facts have emerged that seven Deposit Money Banks (DMBs) are already competing in not only adoption but performance efficiency and customer satisfaction in the use of Artificial Intelligence-powered chatbots. The banks are Zenith Bank with chatbot called Ziva; Fidelity bank plc's Ivy; First City Monument Bank's Temi; UBA Group with its Leo, an AI-powered Facebook Messenger bot that allow users perform banking transactions; Access Bank's Tamada; Heritage Bank's octopus chatbot and Keystone Bank's chatbot called oxygen. A chatbot is a computer programme or an artificial intelligence which conducts a conversation through audio or text. They are messaging apps which allow businesses and brands to remain online 24 hours, providing customer support by instant responses and complaint resolution. AI, chatbots and automated, self-service technologies free up call centre employees from routine tier-1 support requests so they can focus on more complex tasks.

Making new friends, or even catching up with old ones, can be hard enough when you're 93 years old and living in fear of the coronavirus. Which is why Chui Dip-sai has been so grateful for Temi, an Israeli-developed service robot that has become the Hong Kong nursing home resident's constant companion after city-wide lockdowns have kept family and friends away. "It is very easy to use," Chui told the Nikkei Asian Review in a video interview facilitated by the Cantonese-speaking robot on wheels. "I always talk with Temi, and I don't feel bored anymore." With a tablet-like screen for a face, and voice control powered by Google, Temi can keep Chui entertained for hours with music, videos and other diversions.

The pace of content creation has never been faster. Each day, the world generates 2.5 quintillion bytes of data, and more than 90 percent of all data in existence has been produced since 2016. In the process, it is being hidden from search engines, locked away in multimedia formats that cannot be catalogued. A gold mine of information is waiting to be tapped if only the spoken word could be easily converted to text. Doing so by hand is time consuming and, often, prohibitively expensive.

Usually, CES robots are a little sad, but Temi is different. Instead of pretending their robot can do a bunch of things it can't, like hold a conversation, the team at Temi focused on the things it can. The Temi Robot has 16 sensors (including LiDAR) that help it recognize people and map out your home. With the tap of a button it can follow you, or go anywhere you ask it. When it gets there, it can play music or media, wirelessly charge devices, act as an Alexa device, and work as a video chat or telepresence bot, among other things.

Is committed to a robotic future that empowers and enhances human abilities, enriches human experiences and delivers intuitive, reliable and fun products that are usable by all. We have worldwide HQs in San Francisco, New York, Tel-Aviv, Shenzhen, and Singapore. The temi team is making this vision a reality.

Temi, the rolling robot Wolf hopes you'll soon have in your home, looks more like a tablet on wheels. Wolf's not trying to build an artificial best friend or a robo-sidekick; instead, Temi was designed to be a video chat and music machine. The actual robot, Temi, stands 3 feet tall and rolls on four small wheels. "We tried hard not to provide Temi a face," Wolf says.