5 accepted papers
The remarkable success of multimodal large language models (MLLMs) has driven advances in multimodal embeddings, yet existing models remain inherently discriminative, limiting their ability to benefit from reasoning-driven generation paradigm. In this work, we pioneer the exploration of generative e…
Recently, large multimodal models (LMMs) have achieved significant advancements. When dealing with high-resolution images, dominant LMMs typically divide them into multiple local images and a global image, leading to a large number of visual tokens. In this work, we introduce AVG-LLaVA, an LMM that…
Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical results indicate that existing LMM-based embedding models trained with the standard InfoNCE loss exhibit a high degree of ov
Text-rich images are ubiquitous in real-world applications, serving as a critical medium for conveying complex information and facilitating accessibility.Despite recent advances driven by Multimodal Large Language Models (MLLMs), existing benchmarks suffer from limited scale, fragmented scenarios, a
Prior research in Image Machine Translation (IMT) has focused on either translating the source image solely into the target language text or exclusively into the target image. As a result, the former approach lacked the capacity to generate target images, while the latter was insufficient in produci…