CiF is a large new civil infrastructure segmentation dataset that shows zero-shot foundation models and domain-supervised models plateau at roughly 25% mAP, establishing infrastructure inspection as an open challenge for current visual AI.
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Yolo26: Key architectural enhancements and performance bench- marking for real-time object detection
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2026 26representative citing papers
OctoSense supplies a large multimodal robotics dataset and a late-fusion masked autoencoder that runs fast and outperforms image-only models on optical flow, depth, segmentation, and ego-motion tasks while remaining robust under sensor degradation.
WHU-Infra3D is a new large-scale multi-modal dataset and benchmark for 3D roadside infrastructure inventory, providing over 175k 2D boxes, thousands of 3D instances, and 181k annotations across five core tasks while exposing cross-city gaps and long-tailed defect vulnerabilities.
VisHarness learns a reinforcement-learned policy to harness specialized visual experts via multi-turn interactions and dynamic visual memory archiving, outperforming general models on four visual reasoning benchmarks.
XWOD is a large-scale real-world benchmark for traffic object detection under seven extreme weather conditions that improves zero-shot generalization to other weather datasets.
A 48-camera residential platform delivers real-time occlusion-robust 3D perception and coordinated actuation for multi-human multi-robot interaction in a shared home workspace.
S2-CoT coordinates a Structural Fidelity Adapter in the encoder-decoder with a Semantic Context Adapter in the entropy model to convert potential performance loss into state-of-the-art gains across base codecs while using only a small fraction of parameters.
New protocols show depth bias at 5m varies from 50mm to over 1400mm across five cameras, with 3D pose error (MPJPE 104-365mm) tracking depth quality while 2D pose accuracy remains comparable (78-90% mAP).
Proposes DERNet with Decompose-Enhance-Reconstruct operator and three plug-and-play modules to shift small object detection from spatial to spectral feature processing, claiming better performance than YOLOv11 with 1/6 the parameters.
A decoupled pipeline with YOLO detection, deterministic prompt encoding, and QLoRA-adapted 1.5B LLM achieves superior structured report generation compared to monolithic VLMs on synthetic maintenance data.
FS-FSD regresses frequency-supervised Fourier contours for bridge defects, yielding higher polygon accuracy and better geometric quality than box, mask, or contour baselines on 3,767 UAV images with 42,346 instances.
A probabilistic maturity model treats ripeness as continuous rather than discrete classes, improving robustness to annotation noise in fruit detection.
CSA-Graphs provides scene graphs and skeleton graphs as privacy-preserving alternatives to real CSAI images, with experiments showing they support classification and improve when combined.
TinyFormer adds Parallel Bi-fusion Module and Spatial Semantic Adapter to a YOLO-DETR hybrid, raising small-object AP by 1.6 points to 58.5% on MS COCO while keeping real-time speed.
MiMuon is a hybrid optimizer that achieves a generalization error bound of O(1/N) independent of the small singular-value gap that limits the original Muon bound, while retaining the same O(1/T^{1/4}) convergence rate.
A CTM-GNN model with EnSRF assimilation and flow-weighted transition matrix fuses floating car data and camera observations to deliver physically consistent, network-wide traffic volume estimates and forecasts, demonstrated with improved accuracy in Manhattan.
Proposes a vision-based human pose estimation and motion prediction pipeline that uses conformal prediction sets to provide valid confidence guarantees for safe human-robot collaboration.
A unified pipeline using OCR, inpainting, and diffusion models restores text in degraded documents on a new synthetic benchmark dataset, evaluated with the proposed UCSM metric.
A method combining pretrained YOLO11, YOLOE-26, and Gaze-LLE models detects student gaze targets in collaborative learning videos with F1-score 0.829 without requiring labeled training data.
PGL-Net decouples dehazing into global distribution rectification and local structural refinement via PAF and DAM modules, claiming SOTA quality on real-world benchmarks with over 10x lower latency than prior SOTA.
YOLO-MD improves underwater marine debris detection by adding a Dual-Branch Convolutional Enhanced Self-Attention module, a lightweight shift operation, and SFG-Loss for class imbalance, achieving 0.875 precision and 0.849 mAP50 on the UODM dataset.
Combining a diffusion model and an image-to-image translation model produces more photorealistic game-engine synthetic images than either alone while keeping semantic labels intact.
AIPC uses AI agents to automate PyTorch-to-QNN/SNPE deployment, completing it in 7-20 minutes for regular vision models at low API cost.
A YOLO26x object detector for 31 UK camera trap classes reports mAP 0.984 at IoU 0.5 on held-out data from the same sites as training.
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