PandaGPT: A new generation of cross-modal AGI intelligence is officially here!
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Recently, researchers from Cambridge, NAIST and Tencent AI Lab launched a cross-modal language model called PandaGPT, which has innovative attempts in the field of artificial intelligence. This technology combines ImageBind's modal alignment capabilities with Vicuna's generation capabilities to handle instruction understanding and following capabilities in six modalities. Although the effect of PandaGPT still has room for improvement, it shows the development potential of cross-modal AGI intelligence.
PandaGPT achieves instruction following capability in six modalities by combining ImageBind's multimodal encoder with Vicuna's large language model. It can receive multimodal inputs simultaneously and naturally combine their semantics. PandaGPT combines multimodal signal processing and natural language processing to accomplish complex tasks such as generating detailed image descriptions, writing stories based on video, and answering questions about audio.
During the training process, PandaGPT used a total of 160k image-based language instruction following data as training data. Each training instance consists of an image and a corresponding set of multiple rounds of dialogue. PandaGPT only updates the new linear projection matrix on the ImageBind encoding results and the additional LoRA weights added to the Vicuna's attention module. The total number of parameters for both parameters is about 0.4% of the Vicuna parameters. Training functions are traditional language modeling targets.
In experiments, PandaGPT demonstrated the ability to understand different modalities. Compared to other multimodal language models, the most outstanding feature of PandaGPT is its ability to understand and naturally combine information from different modalities.
Although PandaGPT has an amazing ability to handle multiple modalities and their combinations, there are some issues that need to be solved when dealing with other modals, such as maintaining a fine-grained amount of information about the remaining modalities. Therefore, in order to improve performance, the future development direction of PandaGPT requires the study of fine-grained feature extraction such as cross-modal attention mechanism, and new benchmarks are needed to evaluate the combined ability of multimodal inputs, and further improvement before the production environment.
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Emoticon: Figure 1, PandaGPT can understand the content of the image. Figure 2: Video understanding. Figure 3, video + audio. Figure 4, picture + audio. The original text is in English, please ignore my translation level [doghead]
