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Recent progress in Large Multimodal Models (LMMs) has demonstrated remarkable capabilities in various multimodal settings, moving closer to the goal of artificial general intelligence. By using large amounts of vision-language data, they enhance LLMs with visual abilities, by aligning vision encoders. However, most open-source LMMs have focused mainly on single-image scenarios, leaving the more complex multi-image scenarios mostly unexplored. This is important because many real-world applications use multi-image capabilities such as thorough multi-image analyses. Given the wide range of computer vision situations and data types, there is a strong need to develop a general framework for LMMs that can work effectively with multi-image, video, and 3D data.
To address these issues, this paper discusses some related works. The first work is Interleaved Image-text data, which gives LMMs two key abilities: multimodal in-context learning (ICL) and instruction-following in real-world multi-image scenarios. Next, Interleaved LMMs, like the closed-source GPT-4V and Gemini, support real-world multi-image applications with top performance. The community has also created open-source LMMs with excellent multi-image skills using diverse public datasets. In the last related work, interleaved benchmarks, several high-quality benchmarks have been developed for various scenarios to evaluate these multi-image abilities of LMMs.
Researchers from ByteDance, HKUST, CUHK, and NTU have proposed LLaVA-NeXT-Interleave, a versatile LMM that can handle various real-world settings such as Multi-image, Multi-frame (videos), Multi-view (3D) while maintaining the performance of the Multi-patch (single-image) performance. These four settings are collectively called M4. A high-quality training dataset, M4-Instruct, with 1177.6 samples is created to enhance LMMs with the M4 capabilities. This dataset covers 14 tasks and 41 datasets across these four domains. Using a single model, LLaVA-NeXT-Interleave shows top results in different multi-image tasks compared to previous state-of-the-art models, while still performing well with single images.
The LLaVA-NeXT-Interleave model is tested on M4. The LLaVA-Interleave Bench is selected to cover a range of in- and out-of-domain tasks while evaluating multi-image. For video evaluation, the tests include NExTQA, MVBench, Video Detailed Description (VDD), and ActivityNet-QA (Act). The results for ActivityNet-QA include both accuracy and GPT scores. Additionally, the model is assessed on VideoChat-GPT (VCG) using five criteria: correctness of information, detail orientation, context understanding, temporal understanding, and consistency. For 3D evaluation, the tests include ScanQA and two tasks from 3D-LLM.
The results for multi-image show that the average performance of LLaVA-NeXT-Interleave is better than earlier open-source models in in- and out-domain tests. After adding DPO, the proposed 7B model achieves top performance on the VDD and VideoChatGPT tests, outperforming the previous LLaVA-NeXTVideo (34B). The LLaVA-NeXT-Interleave only uses multi-view images to understand the 3D world and gets much higher scores in difficult 3D situations compared to 3D-LLM and Point-LLM. For single-image tasks, 307k (40%) of the original LLaVA-NeXT single-image data is added to the Multi-patch (single-image), making the model capable of handling these tasks.
In conclusion, researchers have introduced LLaVA-NeXT-Interleave, a flexible LLM that can handle different real-world settings like multi-image, multi-frame (videos), and multi-view (3D). Researchers emphasized the potential of this model to improve and combine the capabilities of LMMs in various visual tasks. Extensive Experiments in this paper show that LLaVA-NeXT-Interleave sets new high standards in multi-image tasks and performs very well in single-image tasks. This work sets a new standard in the field, opening the door for future advancements in multimodal AI and complex visual understanding tasks.
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“}]] [[{“value”:”Recent progress in Large Multimodal Models (LMMs) has demonstrated remarkable capabilities in various multimodal settings, moving closer to the goal of artificial general intelligence. By using large amounts of vision-language data, they enhance LLMs with visual abilities, by aligning vision encoders. However, most open-source LMMs have focused mainly on single-image scenarios, leaving the more complex
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