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computer vision

3,462 papers

#artificial intelligence Preprint Sep 2026

AirAnchor: Bridging Local and Global Spatial Information for Zero-Shot Aerial Vision-and-Language Navigation

AirAnchor is proposed, a new paradigm that bridges local and global spatial information through spatial anchors and integrates both into a shared navigation framework, enabling comprehensive spatial grounding for decision-making.

Shan-Wei Fan, Bin Zhang, Zhi-Wei Xu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method

AGOS-Bench is introduced, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate aerial discoveries and ground-level verification through UAV-UGV cooperation, and AGOS-Agent is proposed, a training-free and tool-augmented approach to address the AGOS task.

Bokyung Yu, Zi-Mo Chen, Jun-Reng Rao et al. · 3 citations · ⚡1
#artificial intelligence Preprint Sep 2026

CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning

Vision-language models (VLMs) demonstrate strong performance across compositional reasoning benchmarks, which require reasoning over semantic perturbations of objects, attributes, relations, and their interactions. However, our controlled analysis reveals that existing compositionality-aware VLMs exhibit element-specif...

Seongjun Jeong, Minjoon Jung, Woo-Suk Choi et al. · 1 citation
#artificial intelligence Preprint Open access Sep 2026

WSPolypNet: Weakly Supervised Polyp Localization in Colonoscopy Videos

Because dense frame-level annotation of colonoscopy videos is costly, we propose WSPolypNet, a weakly supervised framework for polyp localization using only video-level labels. WSPolypNet employs a 3D convolutional neural network trained with video-level supervision to generate class activation maps (CAMs), which ident...

Giseong Hwang, Minjae Jo, Yeonghyeon Park et al. · 0 citations
#artificial intelligence Preprint Sep 2026

SAFER-Activities: A Dataset for Smart Assessment of Fall Events and Routine Activities

SAFER-Activities is introduced, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios, and cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data.

Diwas Lamsal, Pramod Wickramatilake, J. Moonrinta et al. · 0 citations
#artificial intelligence Preprint Sep 2026

TDDN: Text-aligned Diffused DINO Network for Puzzle Understanding

This work fuse DINOv3 and CleanDIFT representations into a perception encoder and align it with RoBERTa-L, yielding a text-aligned model TDDN that preserves this perceptual advantage and leads on segmentation benchmarks among general-purpose contrastive encoders, including SigLIP.

Harsha Patnala, Debopriyo Banerjee, A. Munot et al. · 0 citations
#artificial intelligence Preprint Sep 2026

SAFIRE: Safety-Critical Benchmark for Fine-grained Fire and Smoke Understanding in Multimodal LLMs

SAFIRE is introduced, a large-scale benchmark for fire-smoke understanding in MLLMs, comprising 83K captioned images from 20 scenarios and 193K multiple-choice VQA (MCVQA) generated from a 9.7K-image subset, spanning 10 evaluation dimensions from basic perception to higher-order reasoning.

Peng-Fei Li, Naufal Suryanto, Si-Cheng Zhang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

VoT: Vision-of-Thought for Unified Multimodal Representation Alignment

Vision-of-Thought (VoT), a framework that introduces a discrete visual-thinking layer between vision-language models (VLMs) and diffusion transformers (DiTs) that improves semantic alignment and provides a structured interface for interpretable and controllable generation is proposed.

Jingxiang Sun, Chao Liao, Zheng-Xiong Luo et al. · 0 citations
#artificial intelligence Preprint Sep 2026

TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking

By simplifying overcomplicated motion-centric designs, TFTrack establishes a new minimalist paradigm for efficient 3D point cloud tracking, paving the way for real-time and resource-efficient deployment in embedded robotic systems, such as autonomous vehicles.

Zhao-Feng Hu, Si-Fan Zhou, Jia-Hao Nie et al. · 2 citations

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Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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