Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers for sequence prediction. They apply uni-directional attention to capture sequential dependencies and generate task sequences recursively. However, such autoregressive Transformers may not fit vision tasks well, as vision task sequences usually lack the sequential dependencies typically observed in natural languages. In this work, we design Masked AutoDecoder (MAD), an effective multitask vision generalist. MAD consists of two core designs. First, we develop a parallel decoding framework that introduces bi-directional attention to capture contextual dependencies comprehensively and decode vision task sequences in parallel. Second, we design a masked sequence modeling approach that learns rich task contexts by masking and reconstructing task sequences. In this way, MAD handles all the tasks by a single network branch and a simple cross-entropy loss with minimal task-specific designs. Extensive experiments demonstrate the great potential of MAD as a new paradigm for unifying various vision tasks. MAD achieves superior performance and inference efficiency compared to autoregressive counterparts while obtaining competitive accuracy with task-specific models. Code will be released at https://github.com/hanqiu-hq/MAD.

Masked Autoencoder (MAE) has demonstrated superb pre-training efficiency for vision Transformer, thanks to its partial input paradigm and high mask ratio (0.75). However, MAE often suffers from severe performance drop under higher mask ratios, which hinders its potential toward larger-scale vision Transformers. In this work, we identify that the performance drop is largely attributed to the over-dominance of difficult reconstruction targets, as higher mask ratios lead to more sparse visible patches and fewer visual clues for reconstruction. To mitigate this issue, we design Efficient MAE that introduces a novel Difficulty-Flatten Loss and a decoder masking strategy, enabling a higher mask ratio for more efficient pre-training. The Difficulty-Flatten Loss provides balanced supervision on reconstruction targets of different difficulties, mitigating the performance drop under higher mask ratios effectively. Additionally, the decoder masking strategy discards the most difficult reconstruction targets, which further alleviates the optimization difficulty and accelerates the pre-training clearly. Our proposed Efficient MAE introduces 27% and 30% pre-training runtime accelerations for the ViT-Large and ViT-Huge models, provides valuable insights into MAE’s optimization, and paves the way for larger-scale vision Transformer pre-training. Code and pre-trained models will be released.