arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.
By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan
arXiv:2609.40362v1 Announce Type: new
Abstract: We present Multimodal Flow, a fully continuous generative model of language and vision. Most unified multimodal models either model both language and q...
By Hongyuan Tao, Xinggang Wang, Lianghui Zhu, Yongkang Li, Yunchao Wei, Bin Feng, Shaoyu Chen, Qian Zhang, Chang Huang, Kai Yu
The paper introduces the Meta Convolution Layer (MCL), a dynamic convolution approach that models kernels as input‑conditioned high‑order polynomial functions instead of linear mixtures of basis kernels. MCL generates a single adaptive kernel through a structured polynomial meta‑network, improving training stability and expressivity. Experiments show significant accuracy gains on ImageNet, CIFAR, and fine‑grained classification tasks when MCL is added to ResNet, Wide‑ResNet, Swin, and ViT backbones.
By Naim Reza, Md Al Amin, Ho Yub Jung
The paper introduces ClusterMixer, a token mixer based on hierarchical clustering in hyperbolic space, designed to be transparent and interpretable. It forms the core of a new vision backbone called HCFormer, which incorporates multiple clustering strategies to maintain strong performance. Experiments show HCFormer surpasses existing backbones on tasks such as image classification, object detection, instance segmentation, and semantic segmentation.
By Jianan Wei, Guikun Chen, Zhiyuan Weng, Chunchao Guo, Yujia Wang, Wenguan Wang
arXiv:2606. 27095v1 Announce Type: cross Abstract: Cold-start exemplar-free class-incremental learning requires learning a growing set of classes without replay, external pretraining, or a large initial task.
By Augustinas Ju\v{c}as, Yangchen Pan
arXiv:2606. 31135v1 Announce Type: cross Abstract: We present LINet (Linear Integration Network), a Multi-Stream Neural Network (MSNN) for RGB-D scene classification.
By Gabriel Clinger