ADDP: A General Representation Learning Framework for Both Image Recognition and Generation

核心概念

ADDP proposes an Alternating Denoising Diffusion Process that bridges pixel and token spaces, enabling the learning of general representations applicable to both image recognition and generation tasks.

要約

The paper introduces ADDP, a general representation learning framework that can be applied to both image recognition and generation tasks. The key insights are:

1. Pixels as inputs are crucial for recognition tasks, as they preserve spatially sensitive information better than VQ tokens.
2. VQ tokens as reconstruction targets are beneficial for generation tasks, as they can help the model eliminate imperceptible image details and improve generation quality.

To leverage the advantages of both pixel and token spaces, ADDP proposes an Alternating Denoising Diffusion Process. At each step, the model first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels. The diffusion process gradually masks out a portion of VQ tokens to construct the training samples.

The learned representations from ADDP can be used for both image generation and various recognition tasks, including classification, detection, and segmentation. Extensive experiments show that ADDP achieves competitive performance on unconditional generation, ImageNet classification, COCO detection, and ADE20k segmentation, outperforming previous methods designed for either recognition or generation alone.

統計

The ImageNet-1k dataset is used for pre-training.
FID and IS scores are reported on the ImageNet-1k 256x256 validation set for unconditional generation.
Top-1 accuracy is reported for ImageNet-1k classification after fine-tuning.
AP for object detection on COCO test-dev set and mIoU for semantic segmentation on ADE20k validation set are reported.

引用

"Pixels as inputs are crucial for recognition tasks. Pixels preserve spatially sensitive information better than VQ tokens, which is particularly useful for dense recognition tasks."
"VQ tokens as reconstruction targets are beneficial for generation tasks. Previous works such as (van den Oord et al., 2017; Rombach et al., 2022) show that compared to generating raw pixels, predicting VQ tokens can help the model eliminate imperceptible image details, mitigating the optimization difficulty and resulting in better image generation quality."

抽出されたキーインサイト

ADDP

by Changyao Tia... 場所 arxiv.org 04-03-2024

https://arxiv.org/pdf/2306.05423.pdf

深掘り質問

What other types of representations beyond pixels and VQ tokens could be explored to further improve the unified performance on both recognition and generation tasks

To further enhance the unified performance on both recognition and generation tasks, exploring representations beyond pixels and VQ tokens could be beneficial. One promising approach is to incorporate spatial attention mechanisms into the model. By integrating spatial attention, the model can focus on relevant regions of the image or data, allowing for more precise and context-aware feature extraction. This can help improve the model's ability to capture intricate details and relationships within the data, leading to enhanced performance on both recognition and generation tasks. Additionally, exploring hierarchical representations that capture multi-scale information could also be valuable. By incorporating representations at different levels of abstraction, the model can better understand the data's complexity and generate more diverse and detailed outputs.

How can the proposed alternating denoising process be extended to handle more complex data modalities, such as text-image or audio-visual data

The proposed alternating denoising process can be extended to handle more complex data modalities, such as text-image or audio-visual data, by adapting the framework to suit the specific characteristics of each modality. For text-image data, the model can alternate between denoising text embeddings and image features, leveraging the complementary information from both modalities to improve performance on tasks like image captioning or text-to-image generation. Similarly, for audio-visual data, the model can alternate between denoising audio representations and visual features, enabling the generation of synchronized audio-visual content or enhancing tasks like video captioning. By customizing the denoising process and input modalities, the framework can effectively handle diverse and complex data types.

Given the success of ADDP on both recognition and generation, how can the learned representations be leveraged for other downstream tasks like image editing, video generation, or multi-modal understanding

The learned representations from ADDP can be leveraged for a variety of downstream tasks beyond recognition and generation. For image editing, the representations can be used to manipulate images in a controlled manner, enabling tasks like style transfer, image enhancement, or object removal. In video generation, the representations can be extended to temporal data, allowing for the generation of coherent and realistic video sequences. Additionally, for multi-modal understanding, the representations can facilitate tasks that require the fusion of information from different modalities, such as audio-visual sentiment analysis or cross-modal retrieval. By leveraging the rich and general representations learned by ADDP, these downstream tasks can benefit from improved performance and enhanced capabilities.