CVPR 2024 Papers

The remarkable performance of Vision Transformers (ViTs) typically requires an extremely large training cost. Existing methods have attempted to accelerate the training of ViTs yet typically disregard method universality with accuracy dropping. Meanwhile they break the training consistency of the original transformers including the consistency of hyper-parameters architecture and strategy which prevents them from being widely applied to different Transformer networks. In this paper we propose a novel token growth scheme Token Expansion (termed ToE) to achieve consistent training acceleration for ViTs. We introduce an "initialization-expansion-merging" pipeline to maintain the integrity of the intermediate feature distribution of original transformers preventing the loss of crucial learnable information in the training process. ToE can not only be seamlessly integrated into the training and fine-tuning process of transformers (e.g. DeiT and LV-ViT) but also effective for efficient training frameworks (e.g. EfficientTrain) without twisting the original training hyper-parameters architecture and introducing additional training strategies. Extensive experiments demonstrate that ToE achieves about 1.3x faster for the training of ViTs in a lossless manner or even with performance gains over the full-token training baselines. Code is available at https://github.com/Osilly/TokenExpansion.

Deep learning-based methods have achieved significant successes on solving the blind super-resolution (BSR) problem. However most of them request supervised pre-training on labelled datasets. This paper proposes an unsupervised kernel estimation model named dynamic kernel prior (DKP) to realize an unsupervised and pre-training-free learning-based algorithm for solving the BSR problem. DKP can adaptively learn dynamic kernel priors to realize real-time kernel estimation and thereby enables superior HR image restoration performances. This is achieved by a Markov chain Monte Carlo sampling process on random kernel distributions. The learned kernel prior is then assigned to optimize a blur kernel estimation network which entails a network-based Langevin dynamic optimization strategy. These two techniques ensure the accuracy of the kernel estimation. DKP can be easily used to replace the kernel estimation models in the existing methods such as Double-DIP and FKP-DIP or be added to the off-the-shelf image restoration model such as diffusion model. In this paper we incorporate our DKP model with DIP and diffusion model referring to DIP-DKP and Diff-DKP for validations. Extensive simulations on Gaussian and motion kernel scenarios demonstrate that the proposed DKP model can significantly improve the kernel estimation with comparable runtime and memory usage leading to state-of-the-art BSR results. The code is available at https://github.com/XYLGroup/DKP.

Building accurate maps is a key building block to enable reliable localization planning and navigation of autonomous vehicles. We propose a novel approach for building accurate 3D maps of dynamic environments utilizing a sequence of LiDAR scans. To this end we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation we can extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids a globally shared decoder and time-dependent basis functions which can be jointly optimized in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete large-scale 3D maps outperforming several state-of-the-art methods for static map generation and scene reconstruction.