Identity-consistent generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference identity. Yet, the scarcity of large-scale paired datasets containing multiple images of the same individual forces most approaches to adopt reconstruction-based training. This reliance often leads to a failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, or lighting. Such over-similarity undermines controllability and limits the expressive power of generation. To address these limitations, we (1) construct a large-scale paired dataset MultiID-2M, tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce a benchmark that quantifies both copy-paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, a diffusion-based model that effectively mitigates copy-paste while preserving high identity similarity. Extensive qualitative and quantitative experiments demonstrate that WithAnyone significantly reduces copy-paste artifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive controllable generation.

Every family and friendship group has that one person who is always the designated photographer. If that's you, you'll be happy to hear that the days of missing out on being in group photos are finally a thing of the past. Google's Pixel 9 smartphones go on sale this week, and there's one new tool that people can't wait to try - Add Me. As the name suggests, Add Me allows photographers to add themselves into group snaps, using artificial intelligence (AI). Ahead of its release tomorrow, Google sent MailOnline's Shivali Best the Google Pixel 9 Pro XL so she could try Add Me for herself - with hilarious results.

Google smartphones will no longer require an outstretched arm to get everyone in a group photo. Instead, users will now be able to take a photo from behind the camera – and simply add themselves in using AI. It is one of a host of powerful AI-enabled tools Google announced on Tuesday would feature in its latest Pixel 9 range of smartphones. To use the new'Add Me' tool, users first need to choose one person to take the group photo. They then hand the phone over to another member of the group, who takes a second shot of the same scene, this time with them in it.

In learning from label proportions (LLP), the instances are grouped into bags, and the task is to learn an instance classifier given relative class proportions in training bags. LLP is useful when obtaining individual instance labels is impossible or costly. In this work, we focus on the case of small bags, which allows to design an algorithm that explicitly considers all consistent instance label combinations. In particular, we propose an EM algorithm alternating between optimizing a general neural network instance classifier and incorporating bag-level annotations. Using two different image datasets, we experimentally compare this method with an approach based on normal approximation and two existing LLP methods. The results show that our approach converges faster to a comparable or better solution.

One of the most common applications of machine learning is pattern recognition. Computers that use well-trained algorithms recognize animals in photos, anomalies in stock fluctuations, and signs of cancer in mammograms much better than humans. Let us find out what lies behind this complex process. Pattern recognition is the process of recognizing regularities in data by a machine that uses machine learning algorithms. In the heart of the process lies the classification of events based on statistical information, historical data, or the machine's memory.

Once you take a photo, the Knoto app finds the faces in the image, crops them out, and sends them to the encrypted Knoto server. There, the faces are matched to other known faces, and this data of the person's identity is transmitted back to your phone. The photos are then sent and received by the Knoto app, which ends up being a stream of photos of yourself and your friends or family. If the person being sent the photos doesn't have the app, they get a text with an image and a link to download other photos. In terms of privacy, Lee says that by cropping the photos to only include faces and transmitting those cropped images eliminates a lot of concerns.

Hard to believe, but Google Photos is nearing its first birthday. And boy has it been a success. Photos was launched as a new way to organize and manage your photos on Android, iOS, and within a browser, and in the span of one year it has gained a hefty 200 million users. In celebration of Photos' first birthday, the team behind that app has come up with a few favorite tips and tricks for using the service. One of these involves pressing Shift-? to see a list of keyboard shortcuts useful for navigating Photos.