Despite simpler architectural designs compared with Vision Transformers and Convolutional Neural Networks, Vision MLPs have demonstrated strong performance and high data efficiency for image classification and semantic segmentation. Following pioneering works such as MLP-Mixers and gMLPs, later research proposed a plethora of Vision MLP architectures that achieve token-mixing with specifically engineered convolution- or Attention-like mechanisms. However, existing methods such as S 2 -MLPs and PoolFormers typically model spatial information in equal-sized spatial regions and do not consider cross-scale spatial interactions, thus delivering subpar performance compared with Transformer models that employ global token-mixing. Further, these MLP token-mixers, along with most Vision Transformers, only model 1- or 2-axis correlations among space and channels, avoiding simultaneous 3- axis spatial–channel mixing due to its computational demands. We therefore propose CS-Mixer, a hierarchical Vision MLP that learns dynamic low-rank transformations for tokens aggregated across scales, both locally and globally. Such aggregation allows for token-mixing that explicitly models spatial–channel interactions, made computationally possible by a multi-head design that projects to low-dimensional subspaces. The proposed methodology achieves competitive results on popular image recognition benchmarks without incurring substantially more compute. Our largest model, CS-Mixer-L, reaches 83.2% top-1 accuracy on ImageNet-1k with 13.7 GFLOPs and 94 M parameters.

The rapid progress of Artificial intelligence in generative modeling is marred by widespread misuse. In response, researchers turn to use-based restrictions—contractual terms prohibiting certain uses—as a “solution” for abuse. While these restrictions can be beneficial to artificial intelligence governance in API-gated settings, their failings are especially significant in open-source models: not only do they lack any means of enforcement, but they also perpetuate the current proliferation of tokenistic efforts toward ethical artificial intelligence. This observation echoes growing literature that points to useless efforts in “AI ethics,” and underscores the need to shift from this paradigm. This article provides an overview of these drawbacks and argues that researchers should divert their efforts to studying deployable, effective, and theoretically grounded solutions like watermarking and model alignment from human feedback to effect tangible changes in the current climate of artificial intelligence.

Despite their simpler information fusion designs compared with Vision Transformers and Convolutional Neural Networks, Vision MLP architectures have demonstrated strong performance and high data efficiency in recent research. However, existing works such as CycleMLP and Vision Permutator typically model spatial information in equal-size spatial regions and do not consider cross-scale spatial interactions. Further, their token mixers only model 1- or 2-axis correlations, avoiding 3-axis spatial-channel mixing due to its computational demands. We therefore propose CS-Mixer, a hierarchical Vision MLP that learns dynamic low-rank transformations for spatial-channel mixing through cross-scale local and global aggregation. The proposed methodology achieves competitive results on popular image recognition benchmarks without incurring substantially more compute. Our largest model, CS-Mixer-L, reaches 83.2% top-1 accuracy on ImageNet-1k with 13.7 GFLOPs and 94 M parameters.

Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately. Their representation learning is based on parts of the whole volume whereas their labeling result is produced by aggregation of partial segmentation. Learning and inference with incomplete information could lead to sub-optimal final segmentation result. To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume. The framework makes use of complete information in each volume and can be implemented easily. An effective instance in this framework is given subsequently. We adopt the 3D high-resolution network (HRNet) for learning spatially fine-grained representations and the mixed precision training scheme for memory-efficient training. Extensive experiment results on a publicly available 3D MRI brain dataset show that our proposed model advances the state-of-the-art methods in terms of segmentation performance.

Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the perceptual fidelity of inpainted images compared to real images via linear separability in a feature space. Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan.