How do i get rid of the "attachments" image001.png and …
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ASIA: Adaptive 3D Segmentation using Few Image Annotations
We introduce ASIA (Adaptive 3D Segmentation using few Image Annotations), a novel framework that enables segmentation of possibly non-semantic and non-text-describable "parts" in 3D. Our segmentation is controllable through a few user-annotated in-th...
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volum…
Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" encoder-decoder based approaches, we observed that they perform poorly in...
HYPE-EDIT-1: Benchmark for Measuring Reliability in Frontier Image Editing Model…
Public demos of image editing models are typically best-case samples; real workflows pay for retries and review time. We introduce HYPE-EDIT-1, a 100-task benchmark of reference-based marketing/design edits with binary pass/fail judging. For each tas...
peykar/imagecolony
The solution contains a web application which allows users to select a region on a satellite map which has missing, incomplete or unclear imagery data (due to censorship or cloudy status). The user can switch between free providers such as Google, Yahoo and Bi…
ReMamber: Referring Image Segmentation with Mamba Twister
Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cost makes it resource-consuming in capturing long-range visual-language...
Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025
The Event-Enriched Image Analysis (EVENTA) Grand Challenge, hosted at ACM Multimedia 2025, introduces the first large-scale benchmark for event-level multimodal understanding. Traditional captioning and retrieval tasks largely focus on surface-level...
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SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normaliza...
The Soft X-ray Imager (SXI) on-board the THESEUS mission
We summarize in this contribution the capabilities, design status, and the en- abling technologies of the Soft X-ray Imager (SXI) planned to be on-board the THESEUS mission. We describe its central role in making THESEUS a powerful machine to probe t...
6th Place Solution to Google Universal Image Embedding
This paper presents the 6th place solution to the Google Universal Image Embedding competition on Kaggle. Our approach is based on the CLIP architecture, a powerful pre-trained model used to learn visual representation from natural language supervisi...
From Wardrobe to Canvas: Wardrobe Polyptych LoRA for Part-level Controllable Hum…
Recent diffusion models achieve personalization by learning specific subjects, allowing learned attributes to be integrated into generated images. However, personalized human image generation remains challenging due to the need for precise and consis...
Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imagin…
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, m...
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised…
Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded i...
Generating Highly Realistic Images of Skin Lesions with GANs
As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The abil...
Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have...
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