arXiv:2607. 02612v1 Announce Type: cross Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative.
By Aravind Pradeep, Samira Nazari, Mahdi Taheri, Christian Herglotz
arXiv:2607. 00774v1 Announce Type: cross Abstract: Recent recursive Transformer studies have primarily reused shared parameters across computation steps to construct compact, parameter-efficient models.
By Sang In Lee, Jihun Park
The paper introduces reViT, a recurrent vision transformer that uses a single Transformer block repeatedly to achieve the accuracy of a full-depth encoder while keeping inference FLOPs comparable. It achieves depth‑specific transformations by expressing each block’s feed‑forward network as a convex combination of a small shared expert bank, with a continuous depth coordinate guiding the mixture. Experiments on ImageNet‑1k and DINOv2 distillation show that reViT matches or surpasses state‑of‑the‑art models, reduces stored parameters by about 70%, and supports elastic‑depth deployment from a single checkpoint.
By Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
The paper introduces Gated Token Recurrence (GTR), a softmax‑free recurrent vision backbone that replaces global softmax attention with gated linear attention, alternating scan directions, and enhanced SwiGLU blocks. GTR is distilled from a DINOv3 teacher using only final‑layer patch‑token alignment, and achieves strong performance on COCO object detection (58.9 box AP) with very low latency (1.908 ms on an RTX 4090). The backbone also transfers to multiple dense prediction tasks and runs efficiently on edge hardware via a specialized CUDA operator and TensorRT deployment.
By Zhe Feng, Longfei Liu, Wei Liu, Kai Chen, Jiangjiang Kong, Wei Zhou, Yifeng Qian, Dexiong Chen, Xuanlong Yu, Xi Shen
arXiv:2607. 06982v1 Announce Type: cross Abstract: Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks.
By Hao Kong, Di Liu, Shuo Huai, Xiangzhong Luo, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu
The paper introduces Recursive Block-Diagonal Coupling (RBDC), a training protocol that builds wide vision models by recursively coupling narrower, independently trained models in a parameter‑free block‑diagonal manner. RBDC allows flexible allocation of training budgets across all models and, when applied to vision transformers (DeiT) and convolutional networks (ResNet) on ImageNet, achieves a 30% reduction in FLOPs while maintaining similar test accuracies. Additionally, models trained with RBDC outperform those from existing growth methods at the same training FLOPs and serve as stronger backbones for downstream tasks such as object detection and instance segmentation.
By Maxim Henry, Adrien Deli\`ege, S\'ebastien Pi\'erard, Marc Van Droogenbroeck