arXiv Computer Vision
Sep 24

TEEP-RCNN: Texture-Enhanced Edge-aware Perception for Steel Surface Defect Detection via Improved Convolutional Block Attention in Faster R-CNN

The paper introduces TEEP‑RCNN, a two‑stage detector that augments Faster R‑CNN with a Feature Pyramid Network backbone and an enhanced Convolutional Block Attention Module (CBAM) featuring dropout in the channel attention MLP and batch‑norm in the spatial attention branch. Training employs a differential learning‑rate schedule with cosine‑annealing warm‑up, and inference uses Test‑Time Augmentation combined with Weighted Box Fusion to stabilize localization of elongated and boundary‑adjacent defects. On the NEU‑DET benchmark, TEEP‑RCNN attains 73.3 % mAP@50 and 37.9 % mAP@50‑95 in only ten epochs on a single GPU, matching or surpassing YOLOv11m while excelling on the rolled‑in‑scale defect category under the COCO metric.

By Kirtan Rajesh
arXiv AI
Jun 9

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines

arXiv:2606. 07953v1 Announce Type: new Abstract: Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding.

By Zekai Zhang, Jinglin Zhang, Qinghui Chen, Gang Li, Da Chen, Shuainan Jing, He Wang, Dagang Li, Cong Liu, Cong Bai, Shengyong Chen
arXiv AI
Jun 6

Image Generators are Generalist Vision Learners

arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.

By Valentin Gabeur, Shangbang Long, Songyou Peng, Paul Voigtlaender, Shuyang Sun, Yanan Bao, Karen Truong, Zhicheng Wang, Wenlei Zhou, Jonathan T. Barron, Kyle Genova, Nithish Kannen, Sherry Ben, Yandong Li, Mandy Guo, Suhas Yogin, Yiming Gu, Huizhong Chen, Oliver Wang, Saining Xie, Howard Zhou, Kaiming He, Thomas Funkhouser, Jean-Baptiste Alayrac, Radu Soricut
arXiv Computer Vision
Aug 27

ARGenSeg: Image Segmentation with Autoregressive Image Generation Model

ARGenSeg introduces an autoregressive generation-based approach for image segmentation that integrates seamlessly with multimodal large language models (MLLMs). Unlike prior methods that use boundary points or dedicated segmentation heads, ARGenSeg generates dense masks directly through visual token output and detokenization via a universal VQ‑VAE, enabling fine‑grained pixel‑level perception. The framework employs a next‑scale‑prediction strategy to parallelize token generation, resulting in faster inference while outperforming state‑of‑the‑art segmentation models on multiple datasets.

By Xiaolong Wang, Lixiang Ru, Ziyuan Huang, Kaixiang Ji, Dandan Zheng, Jingdong Chen, Jun Zhou