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Visual understanding is the abstracting of high-dimensional visual signals like images and videos. Many problems are involved in this process, ranging from depth prediction and vision-language…
Generative models, particularly GANs, have demonstrated the ability to encode meaningful visual concepts linearly within their latent space, allowing for controlled image edits, such as altering…
One of the significant challenges in AI research is the computational inefficiency in processing visual tokens in Vision Transformer (ViT) and Video Vision Transformer (ViViT) models.…
Meta has introduced SAM 2, the next generation of its Segment Anything Model. Building on the success of its predecessor, SAM 2 is a groundbreaking unified…
The field of language models has seen remarkable progress, driven by transformers and scaling efforts. OpenAI’s GPT series demonstrated the power of increasing parameters and high-quality…
Artificial intelligence, particularly in training large multimodal models (LMMs), relies heavily on vast datasets that include sequences of images and text. These datasets enable the development…
Video large language models (LLMs) have emerged as powerful tools for processing video inputs and generating contextually relevant responses to user commands. However, these models face…
A significant challenge in the field of visual question answering (VQA) is the task of Multi-Image Visual Question Answering (MIQA). This involves generating relevant and grounded…
Effectively evaluating document instruction data for training large language models (LLMs) and multimodal large language models (MLLMs) in document visual question answering (VQA) presents a significant…
Recently, diffusion models have become powerful tools in various fields, like image and 3D object generation. Their success comes from their ability to handle denoising tasks…