MIC

Visual Language Models (VLMs) have made significant progress in various downstream tasks by developing large-scale multimodal models. However, they sometimes lack reasoning and contextual learning abilities. On the other hand, Large Language Models (LLMs) have revolutionized the NLP community with their strong reasoning and contextual learning capabilities. LLMs can quickly adapt to new tasks involving inference without fine-tuning pre-trained models or parameter updates, such as question answering and commonsense reasoning.

Studying in context learning abilities contributes to VLMs' ability to generalize new knowledge in lifelong learning environments, develop learnable capabilities, and advance artificial intelligence skills. Therefore, we propose the MIC(Multimodality In-Context Learning) dataset. This is a manually constructed instruction tuning dataset supports interleaved text-image inputs, inter-related multiple image inputs, and multimodal in-context learning inputs. By finetuning VLMs on MIC, we enable them to possess multimodal in-context learning capabilities and understand complex relationships between instructions and multiple images.

Overview

News 🚀

1. [09-19] We have converted the MMICL demo to a permanent link: Demo for MMICL. The Vicuna version of MMICL and Chat Mode are presently under development, so they may require careful adjustment of generation parameters and may not work correctly.
2. [09-15] Our paper has been uploaded to arXiv.
3. [09-01] The MIC data has released on the huggingface hub.
4. [08-23] Reach the 1st on MME, 1st on MMBench
5. [08-21] The MMICL-FLANT5XXL and MMICL-Tiny model has released on the huggingface hub.

MMICL

MMICL(Multi-Modal In-Context Learning) is a multimodal vision-language model that incorporates blip2/instrcutblip. It has the ability to analyze and understand multiple images, as well as follow instructions. MMICL outperforms the VL model of the same size and performs exceptionally well on complex visual reasoning datasets. It achieves state-of-the-art performance on both multimodal task leaderboards and a wide range of vision-language tasks. Furthermore, it showcases new capabilities in video understanding and multimodal in-context learning (M-ICL).

🔥 Further details of model and dataset will be released ASAP.

Model: MMICL-FLANT5XXL; MMICL-Tiny

Data: MIC_5M; MIC_Sampled

Demo for MMICL: playground for MMICL-FLANT5XXL

• Capability of multiple images refering and reasoning

• Manually constructed In-context instruction tuning dataset

• Till 21st Aug. 2023 1st on MME, 1st on MMBench

• Visual Encoder: VIT-L from CLIP/ ViT-G/14 from EVA-CLIP

• Pre-trained LLM: FlanT5-XL/ FlanT5-XXL/ Vicuna-7B/ Vicuna-13B

Performance

MME

	Perception(Overall)	Existence	Count	Position	Color	Posters	Celebrity	Scene	Landmark	Artwork	OCR
SCORE	1376.00	175.00	143.33	73.33	171.67	130.95	145.88	152.75	135.08	133.00	115.00
RANK	1	4	2	5	1	7	3	6	8	3	2

	Cognition(Overall)	Commonsense Reasoning	Numerical Calculation	Text Translation	Code Reasoning
SCORE	360.36	117.86	62.50	107.50	72.50
RANK	1	2	5	1	4

MMBench

• test set

Method	Vision Model	Language Model	Overall	LR	AR	RR	FP-S	FP-C	CP
MMICL	EVA-G	FLANT5-XXL	65.2	44.3	77.9	64.8	66.5	53.6	70.6
JiuTian	EVA-G	FLANT5-XXL	64.7	46.6	76.5	66.7	66.5	51.6	68.7

• dev set

Method	Vision Model	Language Model	Overall	LR	AR	RR	FP-S	FP-C	CP
MMICL	EVA-G	FLANT5-XXL	67.9	49.2	77.9	73.0	66.7	57.2	77.2
JiuTian	EVA-G	FLANT5-XXL	67.1	46.7	69.7	71.3	67.3	58.6	75.8

Reproduce Tips

Since experiments reported in our paper are all conducted on NVIDIA DGX-A40 servers (which might be difficult to acquire), we reimplement MMICL with:

• Ubuntu servers with 4/6* NVIDIA GeForce A40 (46G) GPUs
• Install Apex to enable the bf16 training and evaluation
• Cuda 11.3, deepspeed to enable the zero2-offload and zero3 training
• Packages with certain versions (provided below)

Setup

We conduct our experiment with Anaconda3. If you have installed Anaconda3, then create the environment for MMICL:

conda env create -f environment.yml

MIC Data

Using multiple data source such as: VCR, VQAv2, GQA, COCO, NLVR2, OKVQK, FILCKR, STVQA, MSRVTT, MSRVTTQA, TextVQA, RefCOCO, WikiArt, DiffusionDB, VSR, LLaVa-Instruct, MiniImagenet We tranform it into few shot style and stored it into jsonl files: It forms the all data in to multi instruction style with zero to few-shot form data. runing the preprocessed script and change the data into raw arrow file for further training:

# The detail of MIC dataset will be added ASAP
python data_preprocess.py

Format

1. Interleaved Image-Text Data

Input:  Image 0 is <image0> {image 0}
        ...
        Image j is <imagej> {image j}
        {question}

MMICL:  {answer}

2. In−Context Demonstration Data

Input:  Image 0 is <image0> {image 0}.
        {question} 
        {answer} 
        ...
        Image j is <imagej> {image j}.
        {question} 

MMICL:  {answer}

• interleaved image text example

• relevant image example

• in context example

Examples

Model Structure

Training

Run training scripts in run_script :

# For training the MMICL in FLANT5-xxl version
bash run_script/flickr/deep_speed_instructblip_t5xxl.sh

Inference

# For T5 based model
from model.instructblip import InstructBlipConfig, InstructBlipModel, InstructBlipPreTrainedModel,InstructBlipForConditionalGeneration,InstructBlipProcessor
import datasets
import json
import transformers
from PIL import Image
import torch
model_type="instructblip"
model_ckpt="BleachNick/MMICL-Instructblip-T5-xxl"
processor_ckpt = "Salesforce/instructblip-flan-t5-xxl"
config = InstructBlipConfig.from_pretrained(model_ckpt )

if 'instructblip' in model_type:
    model = InstructBlipForConditionalGeneration.from_pretrained(
        model_ckpt,
        config=config).to('cuda:0',dtype=torch.bfloat16) 

image_palceholder="图"
sp = [image_palceholder]+[f"<image{i}>" for i in range(20)]
processor = InstructBlipProcessor.from_pretrained(
    processor_ckpt
)
sp = sp+processor.tokenizer.additional_special_tokens[len(sp):]
processor.tokenizer.add_special_tokens({'additional_special_tokens':sp})
if model.qformer.embeddings.word_embeddings.weight.shape[0] != len(processor.qformer_tokenizer):
    model.qformer.resize_token_embeddings(len(processor.qformer_tokenizer))
replace_token="".join(32*[image_palceholder])

Images:

image = Image.open ("images/cal_num1.png")
image1 = Image.open ("images/cal_num2.png")
image2 = Image.open ("images/cal_num3.png")
images = [image,image1,image2]

prompt = [f'Use the image 0: <image0>{replace_token},image 1: <image1>{replace_token} and image 2: <image2>{replace_token} as a visual aid to help you calculate the equation accurately. image 0 is 2+1=3.\nimage 1 is 5+6=11.\nimage 2 is"']
prompt = " ".join(prompt)

inputs = processor(images=images, text=prompt, return_tensors="pt")

inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)
inputs['img_mask'] = torch.tensor([[1 for i in range(len(images))]])
inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0)

inputs = inputs.to('cuda:0')
outputs = model.generate(
        pixel_values = inputs['pixel_values'],
        input_ids = inputs['input_ids'],
        attention_mask = inputs['attention_mask'],
        img_mask = inputs['img_mask'],
        do_sample=False,
        max_length=50,
        min_length=1,
        set_min_padding_size =False,
)
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)

Output:

    3x6=18"

Images:

image = Image.open ("images/chinchilla.png")
image1 = Image.open ("images/shiba.png")
image2 = Image.open ("images/flamingo.png")
images = [image,image1,image2]
images = [image,image1,image2]
prompt = [f'image 0 is <image0>{replace_token},image 1 is <image1>{replace_token},image 2 is <image2>{replace_token}. Question: <image0> is a chinchilla. They are mainly found in Chile.\n Question: <image1> is a shiba. They are very popular in Japan.\nQuestion: image 2 is']

prompt = " ".join(prompt)

inputs = processor(images=images, text=prompt, return_tensors="pt")

inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)
inputs['img_mask'] = torch.tensor([[1 for i in range(len(images))]])
inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0)

inputs = inputs.to('cuda:0')
outputs = model.generate(
        pixel_values = inputs['pixel_values'],
        input_ids = inputs['input_ids'],
        attention_mask = inputs['attention_mask'],
        img_mask = inputs['img_mask'],
        do_sample=False,
        max_length=50,
        min_length=1,
        set_min_padding_size =False,
)
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)

Output:

   a flamingo. They are native to South America and are known for their bright red plumage and distinctive call.

Images:

image = Image.open ("images/flamingo_photo.png")
image1 = Image.open ("images/flamingo_cartoon.png")
image2 = Image.open ("images/flamingo_3d.png")

images = [image,image1,image2]
prompt = [f'Use the image 0: <image0>{replace_token}, image 1: <image1>{replace_token} and image 2: <image2>{replace_token} as a visual aids to help you answer the question. Question: Give the reason why image 0, image 1 and image 2 are different? Answer:']

prompt = " ".join(prompt)

inputs = processor(images=images, text=prompt, return_tensors="pt")

inputs['pixel_values'] = inputs['pixel_values'].to(torch.bfloat16)
inputs['img_mask'] = torch.tensor([[1 for i in range(len(images))]])
inputs['pixel_values'] = inputs['pixel_values'].unsqueeze(0)

inputs = inputs.to('cuda:0')
outputs = model.generate(
        pixel_values = inputs['pixel_values'],
        input_ids = inputs['input_ids'],
        attention_mask = inputs['attention_mask'],
        img_mask = inputs['img_mask'],
        do_sample=False,
        max_length=80,
        min_length=50,
        num_beams=8,
        set_min_padding_size =False,
)
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
print(generated_text)

Output:

   image 0 is a photo of a flamingo standing in the water, image 1 is a cartoon drawing of a flamingo and image 2 is a low polygon count 3d model animation

Reference

📑 If you find our projects helpful to your research, please consider citing:

@article{zhao2023mmicl,
  title={MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning},
  author={Zhao, Haozhe and Cai, Zefan and Si, Shuzheng and Ma, Xiaojian and An, Kaikai and Chen, Liang and Liu, Zixuan and Wang, Sheng and Han, Wenjuan and Chang, Baobao},
  journal={arXiv preprint arXiv:2309.07915},
  year={2023}
}