arXiv:2606. 25318v1 Announce Type: cross Abstract: In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention.
In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention. Roto-reflection equivariant networks preserve the rotational, flip and positional symmetry in feature maps, making them useful for tasks where orientation of the inputs is relevant to the model outputs.
arXiv:2505. 21736v2 Announce Type: replace-cross Abstract: Translation equivariance is a central reason convolutional neural networks have been successful in computer vision.
arXiv:2606. 27864v1 Announce Type: cross Abstract: Vision transformers have become a dominant architecture for visual recognition.
The paper introduces SAWRD-Net, a symmetry‑aware network for detecting water reflections. It uses dihedral group‑equivariant convolutions, a multi‑scale reflection equivariant block with symmetric attention, and a matrix‑decomposition decoder to regress keypoints on the reflection axis. On the largest water reflection dataset, SAWRD‑Net achieves a 0.890 true‑positive rate, surpassing existing detectors.
arXiv:2607. 04262v1 Announce Type: new Abstract: Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information.
arXiv:2505. 15441v5 Announce Type: replace-cross Abstract: Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and mirror configurations.
Vision Transformers underperform convolutional networks when training data is scarce, and distilling convolutional inductive biases from a CNN teacher is an effective remedy that leaves the deployed model unchanged. General-purpose feature distillation, however, transfers little in this setting.
Vision Foundation Models (VFMs) have significantly advanced dense feature matching, yet severe in-plane rotation remains a critical challenge. Existing solutions face a fundamental dilemma: data-driven methods require inefficient parameter scaling to implicitly learn rotations, whereas strictly equivariant networks lack the semantic capacity of modern VFMs.
arXiv:2509. 11218v2 Announce Type: replace-cross Abstract: Spatial transformations such as rotation and scale obscure the morphological cues needed for accurate image classification.
TriCCOT is a tri-part architecture designed for onboard space object detection that balances computational efficiency with robust performance. It combines a convolutional region proposal network, a conformal prediction stage that enlarges bounding boxes with distribution‑free probabilistic coverage, and Aper‑GATES—a hardware‑friendly attention‑based classifier that replaces standard transformer operations with convolutional projections and gating. Experiments on DIOR and VDVRaw datasets show competitive detection accuracy and improved robustness to blur and noise, and the model was fully deployed on a Xilinx Versal VCK190 FPGA without altering the underlying DPU architecture.
TriCCOT is a tri-part architecture designed for onboard space object detection that balances computational efficiency with robust performance. It combines a convolutional region proposal network, a conformal prediction stage that enlarges bounding boxes with distribution‑free probabilistic coverage, and Aper‑GATES, a hardware‑friendly attention‑based classifier that replaces standard transformer operations with convolutional projections and gating. Experiments on DIOR and VDVRaw datasets show competitive detection accuracy and improved robustness to spatial blur and noise, and the model was fully deployed on a Xilinx Versal VCK190 FPGA without altering the underlying DPU architecture.
arXiv:2606. 07626v1 Announce Type: cross Abstract: Perception in dense, unstructured urban traffic remains a major challenge for autonomous driving because of the wide variety of road users, frequent occlusions, irregular motion patterns, and the lack of standardized road layouts.