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Apple says ChatGPT image generation is now available in Image Playground with new ChatGPT styles, releases an API, adds Genmoji text descriptions, and more (Hartley Charlton/MacRumors)
https://www.macrumors.com/2025/06/09/chatgpt-integration-co…

ChatGPT Integration Coming to Apple's Image Playground
Apple today revealed a number of updates for Image Playground and Genmoji. ChatGPT image generation is now available directly in Image Playground,...

Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Suman Mahapatra, Pradipta Maji
https://arxiv.org/abs/2506.07023

Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images
Segmentation of nuclei regions from histological images enables morphometric analysis of nuclei structures, which in turn helps in the detection and diagnosis of diseases under consideration. To develop a nuclei segmentation algorithm, applicable to different types of target domain representations, image-to-image translation networks can be considered as they are invariant to target domain image representations. One of the important issues with image-to-image translation models is that they fai…

Einmal unterzeichnen bitte
https://www.eu-inc.org/

EU Inc — Sign the petition to create a pan-european startup entity
Let’s unite the European startup ecosystems by creating a pan-european legal entity. We got 6 weeks. Sign now and help promoting!

GenIR: Generative Visual Feedback for Mental Image Retrieval
Diji Yang, Minghao Liu, Chung-Hsiang Lo, Yi Zhang, James Davis
https://arxiv.org/abs/2506.06220

GenIR: Generative Visual Feedback for Mental Image Retrieval
Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind, that is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which…

INXS: Access All Areas
Great Australian Pods Podcast Directory: #GreatAusPods

Maryland-based quantum computing company IonQ agrees to acquire Oxford Ionics, which spun out of Oxford University, in a $1.07B all-stock deal, closing in 2025 (Oliver Barnes/Financial Times)
https://www.ft.com/content/dde7bac2-cacb-4deb-9223-5bcfe285db15

US quantum computing company IonQ to buy Oxford university start-up
Acquisition of Oxford Ionics for $1.1bn shows advances in processing are kick-starting dealmaking

2025 NFL Draft first-round pick contract tracker: Cardinals sign Walter Nolen; Patriots ink Will Campbell

https://www.cbssports.com/nfl/news/2025-…

watchOS 26 bringt Motivationstrainer, neues Design und mehr Komfort
Motivationssprüche direkt ins Ohr – die Apple Watch bekommt mit watchOS 26 neben dem neuen Design einen Fitnesstrainer. Einzelne Funktionen werden komfortabler.

CoMemo: LVLMs Need Image Context with Image Memory
Shi Liu, Weijie Su, Xizhou Zhu, Wenhai Wang, Jifeng Dai
https://arxiv.org/abs/2506.06279 https://…

CoMemo: LVLMs Need Image Context with Image Memory
Recent advancements in Large Vision-Language Models built upon Large Language Models have established aligning visual features with LLM representations as the dominant paradigm. However, inherited LLM architectural designs introduce suboptimal characteristics for multimodal processing. First, LVLMs exhibit a bimodal distribution in attention allocation, leading to the progressive neglect of middle visual content as context expands. Second, conventional positional encoding schemes fail to preser…

Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction
Ambrose Emmett-Iwaniw, Nathan Kirk
https://arxiv.org/abs/2506.05391 https:…

Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction
Autoregressive models are often employed to learn distributions of image data by decomposing the $D$-dimensional density function into a product of one-dimensional conditional distributions. Each conditional depends on preceding variables (pixels, in the case of image data), making the order in which variables are processed fundamental to the model performance. In this paper, we study the problem of observing a small subset of image pixels (referred to as a pixel patch) to predict the unobserve…