Mengyu Zhu

Zhimin Zhu, Jianguo Zhao, Tong Mu et al.

In deep learning, Multi-Layer Perceptrons (MLPs) have once again garnered attention from researchers. This paper introduces MC-MLP, a general MLP-like backbone for computer vision that is composed of a series of fully-connected (FC) layers. In MC-MLP, we propose that the same semantic information has varying levels of difficulty in learning, depending on the coordinate frame of features. To address this, we perform an orthogonal transform on the feature information, equivalent to changing the coordinate frame of features. Through this design, MC-MLP is equipped with multi-coordinate frame receptive fields and the ability to learn information across different coordinate frames. Experiments demonstrate that MC-MLP outperforms most MLPs in image classification tasks, achieving better performance at the same parameter level. The code will be available at: https://github.com/ZZM11/MC-MLP.

Fenglei Hao, Yuliang Yang, Ruiyuan Su et al.

This paper addresses the challenge of capturing global temporaldependencies in long video sequences for Video Object Segmentation (VOS). Existing architectures often fail to effectively model these dependencies acrossextended temporal horizons. To overcome this limitation, we introduce GISE-TTT, anovel architecture that integrates Temporal Transformer (TTT) layers intotransformer-based frameworks through a co-designed hierarchical approach.The TTTlayer systematically condenses historical temporal information into hidden states thatencode globally coherent contextual representations. By leveraging multi-stagecontextual aggregation through hierarchical concatenation, our frameworkprogressively refines spatiotemporal dependencies across network layers. This designrepresents the first systematic empirical evidence that distributing global informationacross multiple network layers is critical for optimal dependency utilization in videosegmentation tasks.Ablation studies demonstrate that incorporating TTT modules athigh-level feature stages significantly enhances global modeling capabilities, therebyimproving the network's ability to capture long-range temporal relationships. Extensive experiments on DAVIS 2017 show that GISE-TTT achieves a 3.2%improvement in segmentation accuracy over the baseline model, providingcomprehensive evidence that global information should be strategically leveragedthroughout the network architecture.The code will be made available at:https://github.com/uuool/GISE-TTT.