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Large Language Models (LLMs) have demonstrated remarkable progress in natural language processing tasks, inspiring researchers to explore similar approaches for text-to-image synthesis. At the same time,…
A major challenge in the evaluation of vision-language models (VLMs) lies in understanding their diverse capabilities across a wide range of real-world tasks. Existing benchmarks often…
Current multimodal retrieval-augmented generation (RAG) benchmarks primarily focus on textual knowledge retrieval for question answering, which presents significant limitations. In many scenarios, retrieving visual information is…
One of the most pressing challenges in the evaluation of Vision-Language Models (VLMs) is related to not having comprehensive benchmarks that assess the full spectrum of…
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…