Key Points
1. Denoising Vision Transformers (DVT) addresses the presence of persistent noise artifacts in Vision Transformer (ViT) outputs, which hinder feature interpretability and disrupt semantic coherence, and proposes a novel noise model to remove position-dependent artifacts from pre-trained ViTs.
2. DVT introduces a two-stage approach, consisting of a noise model to extract artifact-free features and a lightweight denoiser model to predict denoised features from raw ViT outputs, which significantly improves the performance of multiple pre-trained ViTs in semantic and geometric tasks across multiple datasets.
3. The paper investigates the origins of artifacts in ViT outputs and establishes a strong correlation between positional embeddings and the emergence of undesirable artifacts in ViT outputs.
4. DVT reveals that certain ViT training algorithms produce strong artifacts in ViT outputs, while others demonstrate only mild artifacts, and it significantly enhances the performance of both types of pre-trained ViTs across various dense prediction tasks.
5. DVT's per-image denoising method effectively removes artifacts from ViT outputs, yielding visually stunning denoised feature maps, and its lightweight denoiser model generalizes well to unseen data, enabling real-time applications and mitigating distribution shifts.
6. DVT's holistic image semantics representation and spatial artifact feature representation encode spatially independent, artifact-free semantics and position-dependent but input-independent noise using neural fields, removing artifacts and enhancing object clarity in denoised ViTs.
7. DVT introduces a novel noise model tailored for ViT outputs, paired with a neural field-based denoising technique, and develops a streamlined and generalizable feature denoiser for real-time and robust inference, significantly improving the performance of multiple pre-trained ViTs in a range of downstream tasks.
8. DVT's approach does not require expensive re-training of ViTs at scale, as it uses just a single Transformer block for denoising and achieves significant and consistent enhancements in nearly all pre-trained ViTs across various dense prediction tasks post-denoising.
9. DVT's research suggests several avenues for future exploration, including understanding the role of positional embeddings in ViTs, redefining positional embeddings within ViTs and Transformers, and devising methods to denoise pre-trained ViT features without additional training.
Summary
The research paper delves into the emergence of Transformers as a universal architecture for modern foundation models across various modalities, focusing specifically on the challenge of persistent noise artifacts in Vision Transformers (ViTs) outputs. The study investigates the origins of these artifacts, their impact on downstream tasks, and introduces a novel two-stage denoising approach called Denoising Vision Transformers (DVT).
This approach aims to remove noise artifacts from pre-trained ViTs without requiring re-training. The researchers propose a noise model to dissect ViT outputs into semantics, artifact-related terms, and a residual term, achieved through enforcing cross-view feature consistency. Additionally, they introduce a learnable denoiser to predict artifact-free features directly from unprocessed ViT outputs. The method is evaluated on seven representative ViTs, demonstrating significant enhancements in performance across various dense vision tasks. The paper highlights the widespread occurrence of noise artifacts in ViTs, especially linked to the use of positional embeddings, and the efficacy of DVT in addressing this issue.
A visual analysis of ViT output features post-denoising indicates enhanced semantic clarity and object discovery abilities. The study concludes with suggestions for future research directions and acknowledges support from Google for the project.
Reference: https://arxiv.org/abs/2401.02957