ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond (2202.10108v2)

Abstract: Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism. Nevertheless, they treat an image as a 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance, which is instead learned implicitly from large-scale training data with longer training schedules. In this paper, we propose a Vision Transformer Advanced by Exploring intrinsic IB from convolutions, i.e., ViTAE. Technically, ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context using multiple convolutions with different dilation rates. In this way, it acquires an intrinsic scale invariance IB and can learn robust feature representation for objects at various scales. Moreover, in each transformer layer, ViTAE has a convolution block parallel to the multi-head self-attention module, whose features are fused and fed into the feed-forward network. Consequently, it has the intrinsic locality IB and is able to learn local features and global dependencies collaboratively. The proposed two kinds of cells are stacked in both isotropic and multi-stage manners to formulate two families of ViTAE models, i.e., the vanilla ViTAE and ViTAEv2. Experiments on the ImageNet dataset as well as downstream tasks on the MS COCO, ADE20K, and AP10K datasets validate the superiority of our models over the baseline transformer models and concurrent works. Besides, we scale up our ViTAE model to 644M parameters and obtain the state-of-the-art classification performance, i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the best 91.2% Top-1 accuracy on ImageNet real validation set, without using extra private data.

Insights from "ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond"

The advent of Vision Transformers (ViTs) has marked a significant stride in computer vision due to their proficiency in modeling long-range dependencies through self-attention mechanisms. However, a notable drawback of ViTs is their lack of intrinsic inductive biases typically inherent in Convolutional Neural Networks (CNNs), such as locality and scale invariance. These biases are pivotal for efficiently modeling local visual structures and handling scale variance, typically necessitating large-scale datasets and prolonged training schedules to compensate.

The paper "ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond" introduces a novel approach to integrating these intrinsic biases into ViTs. The proposed model, ViTAEv2, utilizes a reduction cell for multi-scale features and a normal cell for locality, marrying the strengths of CNNs with the flexibility of transformers. This marriage results in two model families: vanilla ViTAE and ViTAEv2.

Key Contributions and Results

1. Inductive Bias Integration: ViTAEv2 incorporates locality and scale invariance by redesigning the network structure. By implementing two types of cells:
   • Reduction Cell (RC): This cell is designed to embed multi-scale and local contexts into the tokens, aiding the transformer in learning more efficiently from smaller datasets.
   • Normal Cell (NC): It further models long-range dependencies while maintaining locality.
2. Superior Performance: Extensive experiments validate the superiority of ViTAE models on the ImageNet dataset and various downstream tasks (e.g., MS COCO, ADE20K, and AP10K), outperforming baseline and representative models both in terms of accuracy and data efficiency.
3. Model Scalability: The research scales ViTAE to a base model with 644M parameters, achieving state-of-the-art classification performance, including notable performance on the ImageNet Real validation set without using extra private data.
4. Multi-Stage Design in ViTAEv2: For broader applicability across tasks like object detection and semantic segmentation, ViTAEv2 adopts a multi-stage architecture, which is more adept at multi-scale feature extraction, thereby enhancing performance across various vision tasks.
5. Data and Training Efficiency: A core advantage of ViTAE models is their data efficiency, requiring less data and shorter training durations to achieve competitive performance compared to traditional vision transformers that lack built-in inductive biases.

Implications and Future Directions

The approach of integrating inductive biases into transformers heralds a new direction for enhancing ViTs. By strategically incorporating design elements akin to those proven effective in CNNs, ViTAEv2 balances the strengths of global attention in transformers with the powerful local feature extraction capabilities of convolutions. This melding could signal a shift towards more hybrid architectures in AI model design, optimally blending robust components from varied methodologies.

The implications are manifold: from improving real-time computer vision task efficiency to enhancing model capabilities on diverse downstream applications. Future research could expand upon these insights by further exploring hierarchical architectures, potentially augmenting different biases beyond locality and scale invariance. This could drive improvements in diverse fields such as autonomous driving, medical imaging, and other domains where precise image recognition is crucial.

Overall, ViTAEv2 presents a compelling case for how strategic incorporation of inductive biases into transformer models can significantly enhance performance and adaptability across varied domains, setting the stage for future innovations in AI architecture design.