arXiv:2608.06205v2 Announce Type: replace Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...
arXiv:2608. 20229v1 Announce Type: cross Abstract: Anatomically plausible segmentation remains challenging because of low contrast, ambiguous boundaries, and modality-specific artifacts.
The paper presents a two‑pipeline framework for retinal fundus analysis that combines four‑class disease classification with vessel segmentation. It fine‑tunes eight ImageNet‑pretrained CNNs on the FIVES dataset, applies five gradient‑based explanation methods to assess model interpretability, and benchmarks ten U‑Net variants—including transformer‑based and attention‑enhanced architectures—on the FIVES and DRIVE datasets. The best classification results come from ResNet101 (94.17% accuracy), while the strongest segmentation performance is achieved by Attention U‑Net with a ResNet101V2 backbone, improving DRIVE IoU from 60.80% to 64.83%.
The paper introduces Feature Interaction Network (FINE), a lightweight semantic alignment module for feature fusion networks in object detectors. FINE refines low‑level features using high‑level contextual guidance through cross‑level attention, and employs Alignment‑Aware Token Sampling to reduce attention complexity. The resulting spatial‑channel modulation map selectively enhances semantically relevant pixels while preserving sub‑pixel localization, leading to improved detection accuracy with minimal computational overhead.
arXiv:2608.22679v1 Announce Type: new Abstract: Semantic segmentation has rapidly advanced with deep learning; however, challenges remain in effectively capturing local and global contexts as well as...
arXiv:2608.30371v1 Announce Type: new Abstract: Automatic cardiac image segmentation is pivotal for diagnosing and treating cardiac diseases. In this work, we introduce MCSeg, a volumetric transforme...
FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.
MIMONet is a saliency detection model that uses multi‑scale inputs and outputs to better handle objects of varying sizes. It processes three differently sized images through separate encoder branches that exchange information, allowing each branch to learn size‑variation knowledge from the others. A Multi‑scale Perception module further refines features, and a Joint Saliency Loss ensures consistent, well‑preserved boundaries across the multiple saliency maps produced.
arXiv:2505.16157v3 Announce Type: replace Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...
MARS-CLIP is a zero‑shot semantic segmentation framework that builds on CLIP by adding a multi‑resolution feature extraction module and an attention refinement mechanism. The multi‑resolution module fuses fine‑grained local features with global context to mitigate low spatial resolution, while the attention refinement injects spatial and color biases from intermediate layers into the final self‑attention block to better recover object boundaries. Experiments on six public datasets show that MARS‑CLIP outperforms state‑of‑the‑art methods.
arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.
arXiv:2508. 17254v2 Announce Type: replace-cross Abstract: Higher levels of machine intelligence demand alignment with human perception and cognition.