SPEANet is a parameter‑efficient backbone for remote sensing object detection that incorporates fixed structural operators to enhance contour and frequency responses. It uses stage‑specific prior extraction and context‑conditioned response modulation, assigning smoothed contour and directional modeling to shallow features and a compact approximation‑detail interaction to deeper stages. Experiments on five benchmarks, including DOTA‑v1.0, DOTA‑v1.5, and DIOR‑R, show that SPEANet achieves high mAP scores (up to 78.55%) with only 23.0 M total parameters, of which the backbone accounts for 5.97 M.
By Wei Lu, Junjie Li, Feifei Sang, Si-Bao Chen
LLaVA‑Assessor is a unified large multi‑modal model (LMM) designed for visual quality assessment, combining image and video inputs. It introduces a two‑task framework—quality interpretation and quality scoring—supported by an adaptive architecture, a rigorous human‑annotated dataset, and a machine‑synthesized data expansion pipeline. The model employs a prompt‑disentanglement strategy to stabilize multi‑task training and achieves strong performance across 11 quality scoring test sets and 4 interpretation benchmarks.
By Ziheng Jia, Zicheng Zhang, Jiaying Qian, Guangtao Zhai, Xiongkuo Min
The paper identifies a flaw in current text-to-video safety filters that rely on keyword prompts and per-frame checks, showing that these systems miss harms that arise solely from the ordering of frames. Experiments reveal that a significant portion of generated videos contain harmful content that is undetected because each individual frame appears safe, and prompt engineering does not mitigate this issue. The authors propose an order-aware detector that can distinguish harmful videos from benign shuffles, achieving an AUC of 0.74 compared to chance for per-frame checking.
By Yuxin Cao, Fusen Guo, Yuezhong Wu, Huadong Mo, Wei Song
The paper introduces CoDeR, a new paradigm for world modeling that explicitly builds an executable world using code rather than relying solely on visual observations. CoDeR translates high‑level concepts into structured world rules, executable dynamics, and perceptual observations through five complementary roles, enabling long‑term memory, open‑ended interactions, autonomous world evolution, and persistent multi‑agent dynamics. Experiments show that this framework extends the capabilities of existing world models and achieves state‑of‑the‑art performance across multiple evaluation settings.
By Zixun Fang, Yawen Shao, Kai Zhu, Jie Xiao, Shihan Chen, Yu Liu, Xueyang Fu, Yang Cao, Wei Zhai, Zheng-Jun Zha
Semantically-Guided Domain Randomization (S‑GDR) is an annotation‑free pipeline that uses vision‑language model captioning of a small real reference set, diffusion‑based background synthesis, and mask‑based object composition to generate synthetic training data. In a high‑mix, low‑volume automotive detection benchmark, S‑GDR achieves a mAP50‑95 of 0.739 with only 200 synthetic images, outperforming a domain‑randomized render baseline and several other synthetic data methods under the same budget. These results suggest S‑GDR is a viable alternative for training visual perception systems when annotation, energy, and time resources are severely limited.
By Jose Moises Araya-Martinez, Gautham Mohan, Jens Lambrecht
GeoComposer is a new framework that improves photographic composition by generating textual guidance and visual exemplars for a given image. It uses a geometry-aware representation learning mechanism that incorporates geometric priors from a visual geometry foundation model to maintain global structure and fine-grained correspondences. A reinforcement learning strategy with a hybrid reward optimizes instruction following, aesthetic quality, and geometric consistency, leading to superior results compared to state‑of‑the‑art methods.
By Shuangzhi Li, Qiaoqiao Jia, Xingxin Chen, Guile Wu, Dongfeng Bai
The paper introduces GAD-MambaUNet, a lightweight medical image segmentation network that integrates efficient local modeling, Direction-Group Graph Selective Scan (DG‑GSS) for structured information exchange, and training‑time supervision from a frozen DINOv3 teacher with Gradient‑Adaptive Distillation. GAD‑MambaUNet demonstrates a strong accuracy‑efficiency trade‑off compared to other lightweight and general segmentation methods, and ablation studies confirm the benefits of DG‑GSS and DINOv3‑GAD supervision. Future work aims to refine teacher‑student alignment and apply the framework to multi‑class and multi‑modal medical segmentation tasks.
By Fang Wang, Huitao Li, Wenhan Chao, Zheng Zhuo, Xinxin Yang
The paper introduces NPLSD, a pair of line‑segment detectors optimized for the Neural‑ART NPU on STM32N6 microcontrollers. NPLSD‑H retains a convolutional backbone and replaces transformer components with a fully‑convolutional head, while NPLSD‑M adapts a lightweight trunk to the NPU’s supported operators. Trained on ImageNet and ShanghaiTech Wireframe, the models achieve competitive accuracy with 2.63 M and 0.62 M parameters, respectively, and ablation studies show initialization contributes significantly to performance.
By Parsa Hassani Shariat Panahi, Amir Hossein Jalilvand, M. Hassan Najafi
arXiv:2609.25040v1 Announce Type: cross
Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...
By Sangam Kumar Jena, Pandarasamy Arjunan
arXiv:2609.26792v1 Announce Type: cross
Abstract: Faithfully evaluating end-to-end driving policies in simulation requires observations that are not merely photo-realistic, but preserve the scene fea...
By Ziyang Leng, Sicheng Mo, Seth Z. Zhao, Haoyuan Cai, Yu Zeng, Rowan McAllister, Bolei Zhou
arXiv:2505.01583v2 Announce Type: replace
Abstract: Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLM...
By Jen-Hao Cheng, Yi-Hao Peng, Huapeng Zhou, Vivian Wang, Huayu Wang, Hsiang-Wei Huang, Wenhao Chai, Hou-I Liu, Kuang-Ming Chen, Cheng-Yen Yang, Yi-Ling Chen, Vibhav Vineet, Qin Cai, Jenq-Neng Hwang
arXiv:2509.04600v2 Announce Type: replace
Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
By Zhongyuan Hu, Qijun Ying, Jiazhi Shu, Ronghui Li, Yu Lu, Zijiao Zeng, Xiu Li
arXiv:2602.01163v2 Announce Type: replace
Abstract: Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, tempora...
By Chunliang Hua, Lei Zhang, Jiayang Sun, Chunlan Zeng, Xiao Hu
arXiv:2606.22694v2 Announce Type: replace
Abstract: Vision-Language Models (VLMs) remain unreliable when spatial reasoning requires composing relations whose meanings depend on frames of reference. E...
By Danial Kamali, Tanawan Premsri, Shreya Rajpal, Amir Zadeh, Chuan Li, Parisa Kordjamshidi
arXiv:2607.11509v3 Announce Type: replace
Abstract: Medical image anomaly detection is central to timely diagnosis and clinical decision support, yet abnormal samples are costly to collect because of...
By Zihan Nie, Muhao Xu, Wei Feng, Sijie Niu, Yi Wan, Xunbin Wei, Jianmei Li, Weiye Song, Zongyuan Ge
arXiv:2609.24510v2 Announce Type: replace
Abstract: Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs...
By Xiaoqiang Lu, Licheng Jiao, Lingling Li, Yuting Yang, Long Sun, Wenping Ma, Xu Liu, Fang Liu
MultiViewDx is a physician‑validated multimodal instruction dataset that links medical imaging studies with patient context and normalizes heterogeneous reports into an evidence‑linked workflow (evidence → findings → differential discussion → diagnosis). The dataset covers a wide range of imaging modalities and uses a unified image‑text retriever to ensure that instruction synthesis is grounded in source‑supported evidence. Fine‑tuned models on MultiViewDx achieve the highest average accuracy on four MedVQA benchmarks and receive the strongest overall rating on JAMA Clinical Challenge cases, with ablations confirming the importance of case‑level multi‑view organization and evidence‑linked reasoning.
By Junda Wang, Zonghai Yao, Yujan Ting, Eric Z. Chen, Hieu Tran, Hong Yu, Weijing Huang, Terrence Chen
arXiv:2609.25375v1 Announce Type: cross
Abstract: Global point-cloud registration remains challenging when limited overlap, repetitive geometry, and sensor noise produce correspondence sets dominated...
By Abolfazl Babanazari, Carson Cramer, Tyler Summers, Carlos Nieto, Kaveh Fathian
arXiv:2609.26648v1 Announce Type: cross
Abstract: Active speaker detection (ASD) requires reliable association between visible faces and acoustic speech, yet existing systems often degrade under chal...
By Pu Wang, Yujun Wang, Hugo Van hamme
arXiv:2609.22619v1 Announce Type: new
Abstract: Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains co...
By Nethmi Jayasinghe, Mihir Parashar, Amit Ranjan Trivedi