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In supervised multi-modal learning, data is mapped from various modalities to a target label using information about the boundaries between the modalities. Different fields have been…
The deep learning revolution in computer vision has shifted from manually crafted features to data-driven approaches, highlighting the potential of reducing feature biases. This paradigm shift…
One of the main challenges in current multimodal language models (LMs) is their inability to utilize visual aids for reasoning processes. Unlike humans, who draw and…
In recent years, image generation has made significant progress due to advancements in both transformers and diffusion models. Similar to trends in generative language models, many…
Most LMMs integrate vision and language by converting images into visual tokens fed as sequences into LLMs. While effective for multimodal understanding, this method significantly increases…
Improving image quality and variation in diffusion models without compromising alignment with given conditions, such as class labels or text prompts, is a significant challenge. Current…
SignLLM: A Multilingual Sign Language Model that can Generate Sign Language Gestures from Input Text
The primary goal of Sign Language Production (SLP) is to create sign avatars that resemble humans using text inputs. The standard procedure for SLP methods based…
Despite the advancement of artificial intelligence in the field of medical science, these systems have limited application. This limitation creates a gap in developing AI solutions…
Multimodal Large Language Models (MLLMs) represent an advanced field in artificial intelligence where models integrate visual and textual information to understand and generate responses. These models…
Multimodal large language models (MLLMs) are cutting-edge innovations in artificial intelligence that combine the capabilities of language and vision models to handle complex tasks such as…