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Meet CompAgent: A Training-Free AI Approach for Compositional Text-to-Image Generation with a Large Language Model (LLM) Agent as its Core

[ad_1] Text-to-image (T2I) generation is a rapidly evolving field within computer vision and artificial intelligence. It involves creating visual images from textual descriptions blending natural language processing and graphic visualization domains. This interdisciplinary approach has significant implications for various applications, including digital art,…

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Researchers from ETH Zurich and Microsoft Introduce EgoGen: A New Synthetic Data Generator that can Produce Accurate and Rich Ground-Truth Training Data for EgoCentric Perception Tasks

[ad_1] Understanding the world from a first-person perspective is essential in Augmented Reality (AR), as it introduces unique challenges and significant visual transformations compared to third-person views. While synthetic data has greatly benefited vision models in third-person views, its utilization in tasks involving…

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This AI Paper from China Introduces SegMamba: A Novel 3D Medical Image Segmentation Mamba Model Designed to Effectively Capture Long-Range Dependencies within Whole Volume Features at Every Scale

[ad_1] Enhancing the receptive field of models is crucial for effective 3D medical image segmentation. Traditional convolutional neural networks (CNNs) often struggle to capture global information from high-resolution 3D medical images. One proposed solution is the utilization of depth-wise convolution with larger kernel…

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This AI Paper from NTU and Apple Unveils OGEN: A Novel AI Approach for Boosting Out-of-Domain Generalization in Vision-Language Models

[ad_1] Large-scale pre-trained vision-language models, exemplified by CLIP (Radford et al., 2021), exhibit remarkable generalizability across diverse visual domains and real-world tasks. However, their zero-shot in-distribution (ID) performance faces limitations on certain downstream datasets. Additionally, when evaluated in a closed-set manner, these models…

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Researchers from the Chinese University of Hong Kong and Tencent AI Lab Propose a Multimodal Pathway to Improve Transformers with Irrelevant Data from Other Modalities

[ad_1] Transformers have found widespread application in diverse tasks spanning text classification, map construction, object detection, point cloud analysis, and audio spectrogram recognition. Their versatility extends to multimodal tasks, exemplified by CLIP’s use of image-text pairs for superior image recognition. This underscores transformers’…

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This AI Paper from China Unveils ‘Vary-toy’: A Groundbreaking Compact Large Vision Language Model for Standard GPUs with Advanced Vision Vocabulary

[ad_1] In the past year, large vision language models (LVLMs) have become a prominent focus in artificial intelligence research. When prompted differently, these models show promising performance across various downstream tasks. However, there’s still significant potential for improvement in LVLMs’ image perception capabilities. …

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