arXiv AI July 21, 2026 By Anamitra Ghosh, Abhiroop Chatterjee, Susmita Ghosh

DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification

arXiv:2607. 16338v1 Announce Type: cross Abstract: This article presents DMFNet, a dual-backbone multiscale feature fusion framework with residual feature propagation and spatial attention for remote sensing scene classification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 16

CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection

arXiv:2608.06205v2 Announce Type: replace Abstract: Multispectral object detection combines visible and thermal imagery to improve perception under challenging illumination and environmental conditio...

By Nima Hatami, Karim Faez, Saeed Sharifian, Hamidreza Amindavar

computer-vision

arXiv AI

Jun 16

Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning

arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

multimodal

arXiv Computer Vision

Aug 21

Deep Multimodal Fusion Detection through Spatial Mask and Channel Fusion

arXiv:2608. 02092v2 Announce Type: replace Abstract: Deep multimodal fusion for object detection has demonstrated good performance through mining modal characteristics.

By Guandi Wang, Ming Li, Yunsen Xing, Junle Liu

computer-vision multimodal benchmarks

arXiv AI

Jul 10

MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

arXiv:2511. 12810v2 Announce Type: replace-cross Abstract: Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due to high similarity in color, texture, and size.

By Leena Alghamdi, Muhammad Usman, Hafeez Anwar, Abdul Bais, Saeed Anwar

llms computer-vision benchmarks

arXiv Computer Vision

Aug 25

Deep Multimodal Fusion Detection through Spatial Mask and Channel Competition

The paper introduces an Attention-Driven Complementarity Resampling framework to enhance cross-modality object detection. It employs a shared channel spatial attention mechanism that exchanges semantic masks between modalities, encouraging the backbone to learn generalized features. Additionally, a learnable channel competition module samples and aggregates features channel‑wise, improving robustness and achieving competitive results on multiple datasets.

By Guandi Wang, Ming Li, Yunsen Xing, Junle Liu

computer-vision multimodal benchmarks

arXiv AI

Sep 15

Bi-Level Routing and Sparse Spatial Attention based Multi-View BEV 3D Object Detection for Autonomous Driving

arXiv:2609.14185v1 Announce Type: cross Abstract: Bird's Eye View (BEV)-based multi-view 3D object detection suffers from challenges of computational complexity, multi-scale feature extraction, and e...

By Jing Zhang, Jiaqi Liu, Zibo Wang

agents computer-vision robotics

One email a morning, machine-written

Related stories

CFGPNet: Cross-Attention-Based Fused Gradient Programmed Network Framework for Multispectral Object Detection

Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning

Deep Multimodal Fusion Detection through Spatial Mask and Channel Fusion

MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection

Deep Multimodal Fusion Detection through Spatial Mask and Channel Competition

Bi-Level Routing and Sparse Spatial Attention based Multi-View BEV 3D Object Detection for Autonomous Driving