Category
3,920 papers
LukeNet is proposed, a lightweight CNN integrated with explainable artificial intelligence (XAI) for an IoMT-based SMART Acute Lymphoblastic Leukemia Detection and Management System that achieved an impressive 99% model accuracy as well as 99% unseen test accuracy, higher than six state-of-the-art (SOTA) CNNs.
Experimental results demonstrate that GERIS outperforms existing instance selection methods in terms of classification accuracy and robustness, and offers a transparent, theoretically grounded mechanism for data filtering.
Collaborative medical AI platforms allow researchers to train models on sensitive imaging data while restricting data export. However, trained models can serve as covert carriers of patient information: medical images may be encoded within model parameters and reconstructed outside the secure environment. Existing defe...
This paper proposes self-supervised VOS with Sliding window, Wavelet transform and optimal Transport (SWT), a self-supervised VOS framework entirely trained on static dataset using contrastive learning that achieves excellent results on five VOS datasets as well as an additional body part propagation dataset.
Reach audiences
Advertise in front of researchers, engineers, and readers.
This work uses Fast Fourier Transform (FFT) representation with EfficientNet-B0 for AI-generated image detection and investigates ECA, U-Net, and Attention U-Net as alternatives to this direct encoder-to-classifier approach.
This work introduces MM-VeriRec, a verifiable multimodal recommendation protocol and failure-guided fusion method for hidden visual constraints, image-text mismatch, and impossible-task abstention in agentic multimodal recommendation.
This survey reviews video understanding agents: systems that use video as the primary information source and solve understanding tasks through adaptive state construction and action selection, and identifies open directions toward agentic-native temporal modeling and video-native agents.
A controlled evaluation framework is introduced that disentangles resolution-related performance degradation from task difficulty through semantics-preserving transformations and proposes two simple metrics: Area Under the Scaling Curve (AUSC), which quantifies scaling robustness independent of baseline accuracy, and P...
Results indicate that HRAN can be used to detect disease at an early stage in sericulture, and future work will enhance scalability and efficiency in different environments.
An XAI interface for object detection in industrial manufacturing is introduced based on a fine-tuned vision-language model, designed to generate intuitive explanations for non-expert users, and improvements in explanation clarity, instruction adherence, image groundedness, and contextual awareness are demonstrated.
Current multimodal models are not yet reliable for standalone Eimeria diagnosis without domain-specific fine- tuning and expert validation.
Three pretrained convolutional backbones, ResNet-50, VGG-16, and FaceNet, fine-tuned for PFR by transfer learning under identical preprocessing, splitting, and optimization protocols on the Specs-on-Faces (SoF) dataset are compared.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
