While multimodal models (LMMs) have advanced significantly for text and image tasks, video-based models remain underdeveloped. Videos are inherently complex, combining spatial and temporal dimensions that demand more from computational resources. Existing methods often adapt image-based approaches directly or rely on uniform frame sampling, which poorly captures motion and temporal patterns. Moreover, training large-scale video…

Multimodal large language models (MLLMs) are advancing rapidly, enabling machines to interpret and reason about textual and visual data simultaneously. These models have transformative applications in image analysis, visual question answering, and multimodal reasoning. By bridging the gap between vision & language, they play a crucial role in improving artificial intelligence’s ability to understand and…

High-resolution, photorealistic image generation presents a multifaceted challenge in text-to-image synthesis, requiring models to achieve intricate scene creation, prompt adherence, and realistic detailing. Among current visual generation methodologies, scalability remains an issue for lowering computational costs and achieving accurate detail reconstructions, especially for the VAR models, which suffer further from quantization errors and suboptimal processing…