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ImageBind-LLM: Multi-modality Instruction Tuning
Jiaming Han, Renrui Zhang, Wenqi Shao, Peng Gao, Peng Xu, Han Xiao, Kaipeng Zhang, Chris Liu, Song Wen, Ziyu Guo, Xudong Lu, Shuai Ren, Yafei Wen, Xiaoxin Chen, Xiangyu Yue, Hongsheng Li, Yu Qiao
TL;DR
Existing instruction-tuned language models mainly support language and images, leaving general multimodal instruction following underexplored. ImageBind-LLM aligns LLaMA with ImageBind using efficient visual injection and cache-enhanced inference, and it responds to diverse modalities across evaluated tasks while retaining limitations in visual detail and hallucination control.
Problem
Existing instruction-tuning approaches mainly support language or image inputs, leaving general instruction following across text, image, audio, 3D, and video underexplored.
Method
ImageBind-LLM aligns LLaMA with ImageBind’s image encoder through a learnable bind network, injects transformed features with attention-free zero-initialized gating, and uses a training-free visual cache at inference.
Results
ImageBind-LLM responds to diverse modalities and achieves competitive or superior performance across evaluated vision-language and multimodal benchmark tasks.
Takeaways & Limitations
ImageBind-LLM demonstrates that image-text alignment with ImageBind can support a single language model’s multimodal instruction following without separate training for every modality.
Takeaways & Limitations
The model can hallucinate objects in descriptive instructions, potentially because it injects only one global visual token and therefore receives limited visual information.
Abstract
from arXiv · showhide
We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tuning, different from which, our ImageBind-LLM can respond to multi-modality conditions, including audio, 3D point clouds, video, and their embedding-space arithmetic by only image-text alignment training. During training, we adopt a learnable bind network to align the embedding space between LLaMA and ImageBind's image encoder. Then, the image features transformed by the bind network are added to word tokens of all layers in LLaMA, which progressively injects visual instructions via an attention-free and zero-initialized gating mechanism. Aided by the joint embedding of ImageBind, the simple image-text training enables our model to exhibit superior multi-modality instruction-following capabilities. During inference, the multi-modality inputs are fed into the corresponding ImageBind encoders, and processed by a proposed visual cache model for further cross-modal embedding enhancement. The training-free cache model retrieves from three million image features extracted by ImageBind, which effectively mitigates the training-inference modality discrepancy. Notably, with our approach, ImageBind-LLM can respond to instructions of diverse modalities and demonstrate significant language generation quality. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter.
1 Introduction
ImageBind-LLM addresses the limited scope of prior language- and image-focused instruction tuning by extending instruction following to multiple modalities. It uses ImageBind-guided alignment and efficient visual injection so image-text training supports broader multimodal responses.
- Prior instruction-tuning methods mainly support language or image inputs, while general multimodal instruction following remains underexplored.
- The model aligns ImageBind’s image encoder with LLaMA through a learnable bind network and injects transformed image features into word tokens across all transformer layers.
- Visual conditions are added with an attention-free, trainable zero-initialized gating factor for progressive knowledge injection.
- The method trains on vision-language data, then uses modality-specific ImageBind encoders and a visual cache model during inference to support other modalities.
- ImageBind-LLM responds to image, text, audio, video, and 3D inputs, including embedding-space arithmetic.
2 Related Work
Related work connects pretrained modality encoders to language models and increasingly supports video understanding. ImageBind-LLM extends this direction by conditioning language generation on multiple non-image modalities through a shared embedding space.
- VideoLLM and Video-LLaMA add temporal video reasoning modules, whereas ImageBind-LLM conditions language responses on image, video, audio, and 3D point clouds.
- ImageBind-LLM targets broader multimodal applications by tuning one language model across multiple input modalities rather than specializing only in video.
- Prior systems connect pretrained vision and language encoders through intermediate networks, while contrastive methods align paired modalities in shared embedding spaces.
3 Method
ImageBind-LLM uses a staged, parameter-efficient pipeline that aligns ImageBind with LLaMA using image-text data and enhances inference through cache retrieval. Its design builds on shared multimodal embeddings and zero-gated injection.
- ImageBind supplies a joint embedding space learned by contrastive alignment of image-paired modalities, enabling modality-specific inputs to share representations.
- LLaMA-Adapter provides the parameter-efficient and zero-initialized gating precedent, while ImageBind-LLM uses a simpler attention-free injection strategy.
- A cache model stores training features and labels as a key-value database and retrieves informative values using key-query similarity during inference.
- The method uses vision-language pre-training, multimodality instruction tuning, and cross-modality cache retrieval for enhanced inference.
- ImageBind-LLM fine-tunes LLaMA on ImageBind-encoded images so the model can process other modalities through their respective ImageBind encoders.
LLaMA
ImageBind-LLM aligns ImageBind features with LLaMA and injects them into every layer through an attention-free, zero-initialized mechanism. It extends image-text training to multimodal instruction following and uses cache retrieval to enhance inference across modalities.
- Bind Network: A bind network aligns ImageBind image features with LLaMA’s word-embedding space using cascaded projection blocks with RMSNorm, SiLU activations, and residual connections.The network transforms the ImageBind feature into LLaMA’s feature dimension before multimodal injection.
- Attention-free Zero-initialized Injection: The model adds transformed multimodal features to every word token at all LLaMA transformer layers, using attention-free zero-initialized gating for progressive visual-semantic injection.The gating factor can increase during training, supporting stable early learning.
- Training: ImageBind-LLM uses image-caption and visual-instruction training while partially tuning LLaMA with parameter-efficient methods and keeping multimodal encoders and the bind network frozen.The training pipeline includes language and visual instruction data, with fake zero-valued images used for language-only examples.
- Multimodal Inference: ImageBind and Point-Bind encoders let the model process image, text, audio, video, and 3D point-cloud instructions through their shared embedding space.The model applies modality-specific encoders before the bind network, with Point-Bind supplying the 3D encoder.
- Cache-enhanced Inference: A training-free visual cache retrieves similar ImageBind image features during inference to reduce the discrepancy between image-based training and non-image modality inputs.The cache stores encoded vision-language training examples and enhances query features through top-k retrieval and residual aggregation.
4.1 Training Details
ImageBind-LLM is trained on large-scale open-source image-text pairs alongside language and visual instruction data. Training uses staged pretraining and fine-tuning with noisy but empirically effective data and parameter-efficient implementation settings.
- Datasets: Pretraining combines 940M open-source image-text pairs from datasets including COCO, CC3M, CC12M, SBU, LAION-2B, COYO, and MMC4.The authors extract 20M high-quality pairs from MMC4-Core and 100M from LAION-2B using CLIP alignment scores.
- Datasets: Instruction tuning uses language datasets Alpaca, GPT4LLM, and ShareGPT, plus visual datasets LLaVA and MiniGPT4.Alpaca contains 52K single-turn examples, LLaVA provides 150K visual instructions, and MiniGPT4 contributes a 3.5K image-description dataset.
- Training setup: The model is pretrained for 3 epochs on 32 A100 GPUs and fine-tuned for 4 epochs on 8 A100 GPUs.The reported pretraining batch size and learning rate are 1024 and 4e-4; fine-tuning uses batch size 32 and learning rate 1.25e-4.
4.2 Quantitative Evaluation on Traditional Tasks
ImageBind-LLM is evaluated zero-shot across traditional vision-language tasks and achieves competitive or strong performance across the reported task families. The analysis links its relative strengths to the bind network while identifying limited modality-token capacity as a possible OCR weakness.
- Evaluation scope: 27 datasets cover OCR, key information extraction, image captioning, visual question answering, and knowledge-grounded image description in zero-shot evaluation.The comparisons include BLIP2, InstructBLIP, LLaVA, LLaMA-Adapter, and other vision-language models.
- Evaluation metrics: OCR uses top-1 accuracy, KIE uses entity-level F1, and image captioning uses CIDEr as the evaluation metrics.Table 1 reports these metrics across the OCR, KIE, and image-captioning task groups.
- Evaluation metrics: VQA and KGID use top-1 accuracy except for Visdial, which uses Mean Reciprocal Rank.The VQA evaluation includes nine benchmarks, while KGID uses ScienceQA and VizWiz image-containing samples.
- Results: ImageBind-LLM achieves competitive performance on OCR, image captioning, and KGID, outperforms PandaGPT, and also performs strongly on KIE and VQA.The authors describe the overall zero-shot results as exceptional across the evaluated tasks.
- Analysis: The authors attribute relative performance over PandaGPT partly to ImageBind-LLM’s bind network, which may better align language and modality features than a single projection layer.This is presented as an implementation-level distinction rather than a controlled causal result.
- Analysis: Using one modality token may limit OCR comprehension compared with vision-language models that use at least ten tokens for visual information.The paper presents this token-count disparity as a possible explanation for weaker OCR ability.
4.3 Quantitative Evaluation on MME Benchmark
On MME, ImageBind-LLM is compared with several visual and multimodal language models on perception and cognition tasks. It performs competitively overall, with especially strong results on selected perception subtasks.
- ImageBind-LLM is evaluated on MME’s perception and cognition capabilities against MiniGPT-4, Otter, LLaMA-Adapter, LLaVA, and PandaGPT.MME uses 10 perception subtasks and 4 cognition subtasks, with concise yes-or-no question answering.
- +8.33 and +1.00 scores are the gains over the second-best methods on MME’s ‘Existence’ and ‘Artwork’ tasks, respectively.
- ImageBind-LLM ranks third on ‘Perception’ and surpasses PandaGPT by +133.18 score.
- The model remains competitive despite being fine-tuned from the original LLaMA model rather than a model with pre-initialized language processing abilities.
4.4 Qualitative Analysis
Qualitative analyses show that ImageBind-LLM can caption and follow instructions across image, audio, video, and 3D point-cloud inputs. Its shared embedding space also supports modality mixing for multimodal reasoning.
- ImageBind-LLM generates modality-dependent captions for image, audio, video, and point-cloud inputs after image-text pretraining.The pretraining uses image-text pairs and trains only the bind-network parameters in that stage.
- The model connects non-image modalities to LLMs without retraining, although its pretrained outputs are short rather than long and detailed.
- Instruction tuning enables free-form requests such as writing a poem from audio, introducing a movie, and describing a 3D object.
- Mixed modality tokens support multimodal reasoning, with image-audio, image-point-cloud, and video-audio combinations enriching scene or content understanding.Modality encoders produce tokens, coefficients mix them, and the mixed tokens are passed to the LLM.
4.5 Advanced Applications
Advanced applications demonstrate ImageBind-LLM’s use across bilingual interaction, multimodal generation, chatbot-style tasks, and modality-expert integration. Examples span language, image, audio, video, and 3D inputs.
- Bilingual and generative applications: Joint English-Chinese fine-tuning enables Chinese responses, while Stable Diffusion integration produces image responses alongside language responses.The Chinese visual capability is zero-shot and often lacks long, detailed responses; generated images can carry more input detail.
- Multimodal instruction examples: Multimodal examples cover game, landmark, city, music, movie, sport, and 3D-object instructions across visual, audio, video, and point-cloud inputs.
- Integration with modality experts: Speech-to-text and video object-detection experts provide context or class labels that enhance audio understanding and video detail capture.
- Object detection cascade: Cascading object detectors with ImageBind-LLM associates text responses with image objects and gives object detectors reasoning ability.
- Chatbot applications: The model answers multimodal chatbot queries, including place identification and brief introductions.
4.6 Failure Cases
The reported failure cases concern hallucinated descriptions, limited instruction-tuning data quality, and fragile modality mixing. These issues constrain descriptive reliability and mixed-concept inputs.
- ImageBind-LLM often hallucinates objects in descriptive instructions, such as attributing unshown content to an image.
- The authors attribute hallucinations partly to injecting only one global visual token, which provides less visual information than competing models’ larger token sets.
- The visual instruction data is not high enough quality because LLaVA data was generated by vision experts and GPT-4 without human checks and corrections.
- Modality mixup is fragile when two modalities represent different concepts, and the model is sensitive to the mixing ratio.The cited example mixes a ‘sky’ image with ‘dog barking’ audio.
5 Conclusion
ImageBind-LLM unifies image, audio, 3D point clouds, and video in one instruction model by aligning ImageBind with an LLM. The authors report comparable performance with recent multimodal LLMs and identify more tokens and modalities as future directions.
- ImageBind-LLM unifies image, audio, 3D point clouds, and video into one multimodality instruction model.
- The model is built by aligning ImageBind’s visual encoder with an LLM and using ImageBind’s binding property for inference across modalities.
- Comparable performance with recent multimodal LLMs is reported across 27 vision-language datasets and the MME benchmark.
- Future work includes increasing multimodality tokens and integrating additional modalities.