Source-linked AI summary
PandaGPT: One Model To Instruction-Follow Them All
Yixuan Su, Tian Lan, Huayang Li, Jialu Xu, Yan Wang, Deng Cai
TL;DR
PandaGPT addresses the limited ability of prior systems to understand multiple modalities holistically. It combines ImageBind encoders with Vicuna and, using aligned image-text training, demonstrates instruction following and emergent cross-modal capabilities across six modalities. The model performs tasks including multimodal reasoning and arithmetic composition, though the authors identify several prototype limitations.
Problem
Prior research largely focused on individual modalities or limited modality combinations, leaving systems unable to connect multimodal information holistically.
Method
PandaGPT combines ImageBind multimodal encoders with Vicuna and trains a linear projection matrix plus LoRA weights using aligned image-language instruction data.
Results
PandaGPT instruction-follows across six modalities and demonstrates emergent cross-modal capabilities, including visual and auditory reasoning and multimodal arithmetic composition.
Takeaways & Limitations
PandaGPT can compose information from different modalities, including connecting visual object appearance with corresponding sounds.
Takeaways & Limitations
The authors identify limited training alignment data, coarse one-vector representations for non-text modalities, input-only multimodal use, missing composition benchmarks, and common language-model deficiencies.
Abstract
from arXiv · showhide
We present PandaGPT, an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities. Our pilot experiments show that PandaGPT can perform complex tasks such as detailed image description generation, writing stories inspired by videos, and answering questions about audios. More interestingly, PandaGPT can take multimodal inputs simultaneously and compose their semantics naturally. For example, PandaGPT can connect how objects look in an image/video and how they sound in an audio. To do so, PandaGPT combines the multimodal encoders from ImageBind and the large language models from Vicuna. Notably, only aligned image-text pairs are required for the training of PandaGPT. Thanks to the strong capability of ImageBind in embedding data from different modalities into the same space, PandaGPT displays emergent, i.e. zero-shot, cross-modal behaviors for data other than image and text (e.g., video, audio, depth, thermal, and IMU). We hope that PandaGPT serves as an initial step toward building AGI that can perceive and understand inputs in different modalities holistically, as we humans do. Our project page is at https://panda-gpt.github.io/.
1 Introduction
PandaGPT addresses the gap between isolated modality-specific systems and holistic multimodal understanding. It combines ImageBind and Vicuna to instruction-follow across six modalities using only aligned image-text training data.
- Prior work largely handled text with individual modalities or limited visual combinations, restricting connections across different modalities.
- PandaGPT combines ImageBind multimodal encoders with Vicuna to instruction-follow data from six modalities.The modalities are image/video, text, audio, depth, thermal, and IMU.
- Only aligned image-text pairs are used for training, while ImageBind’s shared embedding space supports cross-modal capabilities beyond image and text.
- PandaGPT generates detailed image descriptions, writes video-inspired stories, answers audio questions, and composes semantics across multimodal inputs.One example connects objects’ visual appearance with corresponding sounds.
2 Related Work
Related work developed language models, multimodal alignment methods, and bridges between pretrained vision and language systems. ImageBind extended shared embedding alignment across six modalities using image-paired data.
- Large language models use massive unlabeled text pretraining and alignment techniques to achieve strong zero- and few-shot NLP generalization.
- CLIP learns a joint image-text embedding space, while Flamingo, BLIP-2, and MAGIC connect pretrained vision-only and language-only models.
- ImageBind learns a joint embedding across image/video, text, audio, depth, thermal, and IMU data using image-paired data.
3 Method
PandaGPT aligns ImageBind with Vicuna through a lightweight trainable interface while freezing their pretrained parameters. Training uses image-language instruction data, enabling the inherited cross-modal binding to support zero-shot modalities.
- PandaGPT combines ImageBind multimodal encoders with Vicuna for vision- and audio-grounded instruction following.
- Training uses 160k image-language instruction-following examples consisting of images and multi-turn instruction-response conversations.
- Only a linear projection matrix and LoRA weights are trained, while ImageBind and Vicuna parameters remain frozen.The trainable parameters are around 0.4% of Vicuna’s parameters.
- The loss is computed only over system responses, and training runs for two epochs with a 5e-4 learning rate and linear decay.
- Although trained only on aligned image-text data, PandaGPT exhibits zero-shot cross-modal capabilities across six modalities through ImageBind’s frozen binding property.
4 Capabilities of PandaGPT
PandaGPT handles individual and combined multimodal tasks across image/video, text, audio, depth, thermal, and IMU inputs. Its capabilities include question answering, creative writing, reasoning, and arithmetic composition across modalities.
- PandaGPT combines information from image/video, text, audio, depth, thermal, and IMU inputs rather than targeting one modality individually.
- Its demonstrated tasks include image/video-grounded question answering and image/video-inspired creative writing.
- PandaGPT supports visual and auditory reasoning across multimodal inputs.
- Multimodal arithmetic combines information from different modalities so outputs reflect concepts from different input parts.Examples include image-audio and video-audio arithmetic.
5 Limitations
PandaGPT remains a research prototype with several identified avenues for improvement. These include training data, feature extraction, generation, evaluation, and common language-model deficiencies.
- Further improvements include adding other alignment data, such as audio-text pairs.
- Using one embedding vector for non-text content limits feature granularity, motivating approaches such as cross-modal attention.
- PandaGPT currently accepts multimodal information as input but does not generate richer multimedia content.
- The paper calls for new benchmarks for multimodal-input composition and notes hallucination, toxicity, and stereotypes as possible deficiencies.
- PandaGPT is a research prototype and cannot be readily used for real-world applications.
6 Examples
The examples cover question answering, creative writing, reasoning, and multimodal arithmetic. They demonstrate image-, video-, and audio-related capabilities, including arithmetic across modality pairs.
- Image-grounded question answering: Figures 2 and 3 show examples of image-grounded question answering.
- Video-grounded question answering: Figure 4 shows an example of video-grounded question answering.
- Image/video-inspired creative writing: Figure 5 shows an example of image/video-inspired creative writing.
- Visual and auditory reasoning: Figures 6–8 show examples of visual and auditory reasoning.
- Multimodal arithmetic: Figures 9 and 10 show multimodal arithmetic examples combining image and audio.
- Multimodal arithmetic: Figures 11 and 12 show multimodal arithmetic examples combining video and audio.