The paper introduces a lightweight two‑stage system for real‑time video anomaly detection. First, YOLO v11n‑pose detects people and extracts seventeen skeletal keypoints in a single forward pass. Second, each cropped person region is encoded with CLIP ViT‑B/32 and compared via cosine similarity to predefined textual descriptions of anomalous behaviors, removing the need for optical flow, separate pose estimators, or density‑based scoring. Experiments on CUHK Avenue, ShanghaiTech Campus, and a custom indoor dataset achieve about 51 FPS on an NVIDIA Titan XP, a 3.36× speedup over a multi‑feature baseline, while preserving high frame‑level AUROC scores (89.26%, 70.26%, and 84.13%).
By Vanodhya G. Warnasooriya, Amir Hajian, Watchara Ruangsang, Supavadee Aramvith
arXiv:2607.10851v2 Announce Type: replace
Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classif...
By Tonmoy Hossain, Atiqur Rahman, Farhana Hossain Swarnali, Miaomiao Zhang
RLG-TPV introduces a multimodal Tri-Perspective View framework that fuses camera, radar, and training‑time LiDAR data for 3D object detection. It uses radar and LiDAR to guide a ray‑deformable attention lift, refining depth distributions and providing geometric supervision for side and front planes, while radar cross‑section awareness spreads evidence spatially. On nuScenes, the method attains 0.4981 mAP and 0.5959 NDS, improving orientation and velocity accuracy by about 32 % and 31 % over the CRN baseline.
By Ahmet Mete Dokgoz, A. Enes Doruk, Hasan F. Ates
arXiv:2608.29374v1 Announce Type: new
Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs)...
By Yu Cheng, Arushi Goel, Hakan Bilen
arXiv:2608.30621v1 Announce Type: cross
Abstract: Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (V...
By Junbeom Hong, Seonghoon Yu, Hyung Rok Jung, Sundong Kim, Jeany Son
GramLoop is a training‑free framework that enhances frozen DINOv3 dense‑prediction models under distribution shift by adding inference computation within the visual backbone. It replays a short transformer window and uses final‑layer cosine‑Gram consistency to control each replay, propagating proposals through the frozen suffix and accepting them via a patchwise gate. Across object detection and semantic segmentation tasks, GramLoop improves performance on all five shifted benchmarks, notably raising COCO‑O mAP by +0.252 and Effective Robustness by +0.250 while maintaining clean ADE20K accuracy.
By Yang Chen, Canyu Shen, Xinzhe Rao, Yuanyi Yan, Yunlu Chen, Meng Tang, Teng Long, Vincent Tao Hu
arXiv:2606.29686v3 Announce Type: replace
Abstract: Self-collision remains a persistent challenge in SMPL-based human pose estimation and motion generation. Under extreme articulations or stochastic...
By Zhengyuan Li, Zeyun Deng, Yifan Shen, Liangyan Gui, Miaolan Xie, Joseph Campbell, Xifeng Gao, Kui Wu, Zherong Pan, Aniket Bera
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and...
The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalization power that leverages the immense pre-training k...
Performance evaluation for surface-water segmentation commonly uses an aggregate metric such as global intersection-over-union (IoU) to rank model configurations. However, a configuration ranking does...
Verifying image authenticity is increasingly difficult, posing serious risks across journalism, law enforcement, and political domains. Most existing forensic methods rely on high-level visual artifac...
iOSPointMapper is a mobile app that performs real‑time, privacy‑conscious sidewalk mapping using on‑device semantic segmentation, LiDAR depth estimation, and fused GPS/IMU data on recent iPhones and iPads. It detects and localizes sidewalk‑relevant features such as traffic signs, traffic lights, and poles, and includes a user‑guided annotation interface for validating outputs before submission. The anonymized data is transmitted to the Transportation Data Exchange Initiative (TDEI), where it integrates with broader multimodal transportation datasets, and evaluations show the app’s potential for enhanced pedestrian mapping.
By Himanshu Naidu, Yuxiang Zhang, Sachin Mehta, Anat Caspi
The paper presents an automated pipeline that generates high‑quality Quranic datasets, including 848 hours of audio and 286,000 annotated utterances, by collecting recitations, segmenting at pause points with a fine‑tuned wav2vec2‑BERT model, transcribing segments, and verifying transcripts using a novel Tasmeea algorithm. It introduces qdat_bench, a benchmark covering phonemes, diacritization, and Tajweed rules, and a custom Quran Phonetic Script (QPS) for encoding Tajweed. A multi‑level CTC model trained on this data achieves a 0.21% phoneme error rate on the test set and 1.94% on qdat_bench, with a 75.8% Tajweed F1 score.
By Abdullah Abdelfattah, Mahmoud I. Khalil, Hazem Abbas
CF-YOLO introduces a real‑time detection framework for camouflaged micro‑defects on industrial components, combining a Context‑Perception Aggregation Module (CPAM) that fuses large‑kernel macro‑texture cues with small‑kernel boundary details, and a Feature Additive Refinement Module (FARM) that globally refines fine‑grained anomaly representations. The authors also release the Copper Tube Defect Dataset (CTDD), a benchmark of 1,847 images with 4,898 annotated defect boxes. Experiments show CF‑YOLO outperforms baseline detectors such as YOLOv11 by 2.2% in mAP@50 and 3.9% in Precision while preserving real‑time speed.
By Xinda Yu, Kunxin Zheng, Chunan Yu, Qingbo Song, Hao Xiao, Ying Zang, Jie Liu
The paper proposes treating the choice of monomial ordering for Gröbner basis computations as a reinforcement learning problem, using domain-informed reward signals that reflect actual computational cost. By training policies over the space of admissible orderings, the authors demonstrate that the learned strategies outperform traditional static heuristics such as GrevLex on benchmark problems from systems biology and computer vision. The resulting policies also resist simplification into interpretable models, suggesting that deep reinforcement learning captures complex geometric structure beyond conventional approaches.
By R. Caleb Bunch, Alperen A. Erg\"ur, Melika Golestani, Jessie Tong, Malia Walewski, Yunus E. Zeytuncu
The paper introduces the first adaptation of the MedSAM2 foundation model for interactive 3D segmentation of interstitial lung disease (ILD) on thoracic CT scans. It evaluates three fine‑tuning strategies and four prompt types—bounding‑boxes, points, lassos, and scribbles—finding that full model fine‑tuning yields the best performance, improving Dice scores by 4.7 percentage points over the baseline. A proof‑of‑concept workflow is presented where MedSAM2 is first initialized with an automatic prior and then refined by radiologist prompts, with all resources released on GitHub.
By Vasilis Dedousis, Lubnaa Abdur Rahman, Lorenzo Brigat{\omicron}, Ethan Dack, Andreas Christe, Christoph Frank, Manuela Funke-Chambour, Justus Roos, Adrian Huber, Lukas Ebner, Stavroula Mougiakakou
arXiv:2608.28429v1 Announce Type: new
Abstract: Event cameras generate asynchronous, sparse data streams with microsecond temporal resolution, but in moderate-to-high motion scenes they can produce a...
By Zahra Rezaee, Catarina Brites, Jo\~ao Ascenso
The paper presents a multimodal 3D convolutional neural network that classifies Alzheimer’s disease using raw OASIS 1 MRI volumes. It fuses structural T1 images with gray matter, white matter, and cerebrospinal fluid probability maps to capture complementary neuroanatomical information. Evaluated with 5‑fold subject‑level cross‑validation, the model achieves a mean accuracy of 72.34 % and an ROC AUC of 0.7781, with GradCAM visualizations highlighting anatomically relevant regions such as the medial temporal lobe and ventricles.
By Md Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid, Abu Saleh Musa Miah, Najmul Hassan, Md Abdur Rahim, Jungpil Shin
The paper introduces a depth‑aware pothole detection framework that fuses RGB‑D sensor data and evaluates five architectures—YOLOv8n, YOLOv8nSeg, YOLOv9t, RTDETRL, and RTDETRX—on the PothRGBD dataset. YOLOv8nSeg achieves the highest detection performance (mAP@50 = 0.9556, mAP@50_95 = 0.6758) and the most accurate depth estimate (2.96 cm), while YOLOv8n offers the fastest inference (3.6 ms) and RTDETRX delivers the highest detection confidence (92.70 %). The study also shows that even after RANSAC orthorectification, bounding‑box models overestimate pothole depth by 0.16–0.21 cm, indicating a structural bias rather than a calibration error.
By Md Monjurul Ahsan Prodhan, Md Nour Hossain
The paper introduces BalSAM, a model that combines the Segment Anything Model (SAM) with Digital Surface Model (DSM) elevation data to improve tree crown instance segmentation from high‑resolution drone imagery. Experiments across boreal plantations, temperate forests, and tropical forests show that while off‑the‑shelf SAM does not beat a custom Mask R-CNN, fine‑tuning SAM end‑to‑end and incorporating DSM information yield promising results, especially for plantation sites.
By M\'elisande Teng, Arthur Ouaknine, Etienne Lalibert\'e, Yoshua Bengio, David Rolnick, Hugo Larochelle