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High latency in time-to-first-token (TTFT) is a significant challenge for retrieval-augmented generation (RAG) systems. Existing RAG systems, which concatenate and process multiple retrieved document chunks to…
Parameter-efficient fine-tuning (PEFT) methods, like low-rank adaptation (LoRA), allow large pre-trained foundation models to be adapted to downstream tasks using a small percentage (0.1%-10%) of the…
Multimodal Large Language Models (MLLMs) have made significant progress in various applications using the power of Transformer models and their attention mechanisms. However, these models face…
Generating accurate and aesthetically appealing visual texts in text-to-image generation models presents a significant challenge. While diffusion-based models have achieved success in creating diverse and high-quality…
The field of multimodal artificial intelligence (AI) revolves around creating models capable of processing and understanding diverse input types such as text, images, and videos. Integrating…
Large vision-language models have emerged as powerful tools for multimodal understanding, demonstrating impressive capabilities in interpreting and generating content that combines visual and textual information. These…
Monte Carlo Simulations take the spotlight when we discuss the photorealistic rendering of natural images. Photorealistic rendering, or, in layman’s words, creating indistinguishable “clones” of actual…
Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for capturing and reasoning over multimodal inputs and can process both images and text. While LVLM are impressive…
The ability of learning to evaluate is increasingly taking on a pivotal role in the development of modern large multimodal models (LMMs). As pre-training on existing…
Introduction Traditional depth estimation methods often require metadata, such as camera intrinsics, or involve additional processing steps that limit their applicability in real-world scenarios. These limitations…