Many branches of biology, including ecology, evolutionary biology, and biodiversity, are increasingly turning to digital imagery and computer vision as research tools. Modern technology has greatly improved their capacity to analyze large amounts of images from museums, camera traps, and citizen science platforms. This data can then be used for species delineation, understanding adaptation mechanisms,…

Large Vision-Language Models (LVLMs) combine computer vision and natural language processing to generate text descriptions of visual content. These models have shown remarkable progress in various applications, including image captioning, visible question answering, and image retrieval. However, despite their impressive performance, LVLMs still face some challenges, particularly when it comes to specialized tasks that require…

Diffusion models have shown to be very successful in producing high-quality photographs when given text suggestions. This paradigm for Text-to-picture (T2I) production has been successfully used for several downstream applications, including depth-driven picture generation and subject/segmentation identification. Two popular text-conditioned diffusion models, CLIP models and Latent Diffusion Models (LDM), often called Stable Diffusion, are essential…

It isn’t easy to generate detailed and realistic 3D models from a single RGB image. Researchers from Shanghai AI Laboratory, The Chinese University of Hong Kong, Shanghai Jiao Tong University, and S-Lab NTU have presented HyperDreamer to address this issue. This framework solves this problem by enabling the creation of 3D content that is viewable,…

Volumetric recording and realistic representation of 4D (spacetime) human performance dissolve the barriers between spectators and performers. It offers a variety of immersive VR/AR experiences, such as telepresence and tele-education. Some early systems use nonrigid registration explicitly to recreate textured models from recorded footage. However, they are still susceptible to occlusions and texture deficiencies, which…

In a groundbreaking move, researchers at Meta AI have tackled the longstanding challenge of achieving high-fidelity relighting for dynamic 3D head avatars. Traditional methods have often needed to catch up when capturing the intricate details of facial expressions, especially in real-time applications where efficiency is paramount. Meta AI’s research team has responded to this challenge…

Transformers were first introduced and quickly rose to prominence as the primary architecture in natural language processing. More lately, they have gained immense popularity in computer vision as well. Dosovitskiy et al. demonstrated how to create effective image classifiers that beat CNN-based architectures at high model and data scales by dividing pictures into sequences of…

Natural language processing (NLP) has entered a transformational period with the introduction of Large Language Models (LLMs), like the GPT series, setting new performance standards for various linguistic tasks. Autoregressive pretraining, which teaches models to forecast the most likely tokens in a sequence, is one of the main factors causing this amazing achievement. Because of…

Text-to-image diffusion models represent an intriguing field in artificial intelligence research. They aim to create lifelike images based on textual descriptions utilizing diffusion models. The process involves iteratively generating samples from a basic distribution, gradually transforming them to resemble the target image while considering the text description. Multiple steps are involved, adding progressive noise to…