Weights & Biases

🌈 Abstract

The article discusses the use of vision models in diverse tasks such as classification, segmentation, captioning, and image generation/editing, including their use in large language models (LLMs) for vision capabilities. It focuses on an open-source reproduction of the paper "Image Captioners Are Scalable Vision Learners Too," which explores training competitive vision models as image "captioners" on noisy datasets, an alternative approach to the typical pre-training on ImageNet or with contrastive techniques like CLIP.

🙋 Q&A

[01] Vision Models and Captioning

1. What are the key objectives and techniques used in the CapPa model?

• The CapPa model is trained using two main objectives:
  • Captioning: Predicting the caption given the image
  • Masked prediction: Masking parts of the caption and predicting the masked tokens
• The paper recommends using a combination of these objectives, with 25% captioning and 75% masked prediction.

2. How does the CapPa model perform compared to other approaches?

• The CapPa model achieves strong performance on zero-shot accuracy on ImageNet, reaching 63% top-1 and 85% top-5 accuracy.
• When fine-tuning only a small text tower (e.g., SigLiT), the model reaches 74% top-1 and 93% top-5 accuracy on ImageNet.
• The CapPa model outperforms open-source models on most categories of the SugarCrepe benchmark, which tests for fine compositional understanding.

3. What are the advantages of the CapPa model compared to CLIP-style models?

• The CapPa model provides a clearer interpretation of its knowledge, as the captioning task requires a detailed understanding of the image.
• The CapPa model can be efficiently used for downstream applications by keeping the vision tower and discarding the text tower.

[02] Model Architecture and Implementation

1. What are the key architectural details of the CapPa model?

• The vision model uses a ViT-L-16 architecture with 24 layers, a hidden dimension of 1024, and an MLP dimension of 3072.
• The text model is a decoder with cross-attention on the images, with 12 layers (half the depth of the vision model).
• The model uses RMSNorm with Normformer positions, GeGLU activations, and keeps all visual tokens and registers for downstream tasks.

2. How is the model trained and optimized for performance?

• The model is trained with a batch size of 8,192 and achieves a training speed of 0.45 seconds per batch, or 1.6 billion samples per day.
• The training uses sharding (FSDP) to distribute the model and data dimensions across devices.
• Weights are stored in float32, with computation in bfloat16 except for attention logits, normalization layers, and loss.

3. What techniques are used to improve the model's performance and interpretability?

• The use of registers in the ViT model helps the model better encode global information, as shown by the attention visualization.
• The captioning task provides a clearer interpretation of the model's knowledge compared to contrastive learning approaches like CLIP.

[03] Benchmarking and Evaluation

1. How does the CapPa model perform on the SugarCrepe benchmark?

• The CapPa model outperforms open-source models on most categories of the SugarCrepe benchmark, which tests for fine compositional understanding.
• The model achieves strong performance on tasks like replacing, swapping, or adding objects, attributes, and relations in a plausible manner.

2. What are the limitations of the evaluation methods used in the article?

• The article mentions that the validation loss uses only the captioning objective, which explains why it is much lower than the training loss.
• The top-1 and top-5 accuracy on ImageNet is computed using the lowest softmax cross-entropy of each possible class against each image, which is slower and may be affected by different token lengths for each class.

3. How does the CapPa model compare to fine-tuned CLIP-style models?

• The article states that fine-tuning a CLIP model (SigLiT style) is expected to perform better than the current method on ImageNet, as the CapPa model is not trained explicitly for that task.