💻 Category

cs

3882 papers

DogWeave: High-Fidelity 3D Canine Reconstruction from a Single Image via Normal Fusion and Conditional Inpainting

DogWeave is a novel framework that reconstructs high-fidelity 3D canine models from a single RGB image by refining parametric meshes into detailed SDF representations via diffusion-enhanced normal optimization and generating view-consistent textures through conditional inpainting, thereby overcoming challenges like self-occlusion and fur detail to outperform existing state-of-the-art methods.

Shufan Sun, Chenchen Wang, Zongfu Yu2026-03-10💻 cs

Med-Evo: Test-time Self-evolution for Medical Multimodal Large Language Models

Med-Evo is a novel self-evolution framework for medical multimodal large language models that leverages label-free reinforcement learning, featuring Feature-driven Pseudo Labeling and Hard-Soft Reward mechanisms, to significantly enhance model performance on unlabeled test data without requiring additional annotated medical datasets.

Dunyuan Xu, Xikai Yang, Juzheng Miao, Yaoqian Li, Jinpeng Li, Pheng-Ann Heng2026-03-10💻 cs

GeoVisA11y: An AI-based Geovisualization Question-Answering System for Screen-Reader Users

The paper introduces GeoVisA11y, an open-source, LLM-based system that enables screen-reader users to interact with geovisualizations through natural language queries, validated by user studies demonstrating its effectiveness in bridging accessibility gaps and revealing distinct interaction patterns.

Chu Li, Rock Yuren Pang, Arnavi Chheda-Kothary, Ather Sharif, Henok Assalif, Jeffrey Heer, Jon E. Froehlich2026-03-10💻 cs

Backdoor4Good: Benchmarking Beneficial Uses of Backdoors in LLMs

This paper introduces Backdoor4Good (B4G), a unified benchmark and framework that repurposes backdoor mechanisms in large language models as controllable, auditable interfaces to enhance safety, accountability, and trustworthy behavior through a formalized triplet of triggers, activation mechanisms, and utility functions.

Yige Li, Wei Zhao, Zhe Li, Nay Myat Min, Hanxun Huang, Yunhan Zhao, Xingjun Ma, Yu-Gang Jiang, Jun Sun2026-03-10💻 cs

Agentic AI-Driven UAV Network Deployment: A LLM-Enhanced Exact Potential Game Approach

This paper proposes a dual spatial-scale UAV network optimization framework that combines exact potential game algorithms for link configuration and deployment with a large language model to dynamically generate utility weights, thereby enhancing adaptability and performance in terms of energy efficiency, latency, and throughput.

Xin Tang, Qian Chen, Binhan Liao, Yaqi Zhang, Jianxin Chen, Changyuan Zhao, Junchuan Fan, Junxi Tian, Xiaohuan Li2026-03-10💻 cs

"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work

This paper reveals a critical expectation gap in the freelance economy where workers often withhold AI use due to a mistaken belief that clients can detect it, while clients prefer proactive disclosure and lack clear policies, ultimately highlighting the urgent need for standardized guidelines to rebuild trust and accountability in AI-mediated work.

Angel Hsing-Chi Hwang, Senya Wong, Baixiao Chen, Jessica He, Hyo Jin Do2026-03-10💻 cs

Where Do LLM-based Systems Break? A System-Level Security Framework for Risk Assessment and Treatment

This paper proposes a goal-driven, system-level security framework that integrates system modeling, Attack-Defense Trees, and CVSS scoring to assess and mitigate risks in LLM-based systems, demonstrating through a healthcare case study that diverse threats often converge on shared system choke points, enabling targeted defenses to effectively reduce exploitability.

Neha Nagaraja, Hayretdin Bahsi2026-03-10💻 cs

Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment

This paper proposes a human-centred out-of-distribution spectrum that redefines perceptual difficulty based on human accuracy to enable principled comparisons of model-human error alignment, revealing that while vision-language models show the most consistent alignment across conditions, the relative performance of CNNs and ViTs depends on the specific regime of perceptual challenge.

Binxia Xu, Xiaoliang Luo, Luke Dickens, Robert M. Mok2026-03-10💻 cs

Selective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detection

This paper introduces MonoSTL, a selective transfer learning framework that addresses the negative transfer caused by modality gaps in cross-modality distillation for monocular 3D object detection by employing similar architectures and novel depth-aware selective distillation modules to effectively transfer LiDAR depth information to image-based networks, achieving state-of-the-art performance on KITTI and NuScenes benchmarks.

Rui Ding, Meng Yang, Nanning Zheng2026-03-10💻 cs

Classifying Novel 3D-Printed Objects without Retraining: Towards Post-Production Automation in Additive Manufacturing

This paper introduces the ThingiPrint dataset and a contrastive fine-tuning approach that enables the classification of novel 3D-printed objects using their CAD models without requiring model retraining, thereby addressing a critical bottleneck in automating industrial post-production workflows.

Fanis Mathioulakis, Gorjan Radevski, Silke GC Cleuren, Michel Janssens, Brecht Das, Koen Schauwaert, Tinne Tuytelaars2026-03-10💻 cs

FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation

FedEU is a novel federated learning framework that enhances remote sensing image segmentation by integrating evidential uncertainty quantification and client-specific feature embeddings to guide adaptive global aggregation, thereby improving model robustness and reliability across heterogeneous distributed datasets.

Xiaokang Zhang, Xuran Xiong, Jianzhong Huang, Lefei Zhang2026-03-10💻 cs

Give Them an Inch and They Will Take a Mile:Understanding and Measuring Caller Identity Confusion in MCP-Based AI Systems

This paper reveals that MCP-based AI systems are fundamentally insecure due to a lack of caller identity authentication, which allows persistent authorization states and missing per-tool checks to enable unauthorized access to sensitive operations by untrusted callers.

Yuhang Huang, Boyang Ma, Biwei Yan, Xuelong Dai, Yechao Zhang, Minghui Xu, Kaidi Xu, Yue Zhang2026-03-10💻 cs

RobustSCI: Beyond Reconstruction to Restoration for Snapshot Compressive Imaging under Real-World Degradations

This paper introduces RobustSCI, a pioneering framework that shifts snapshot compressive imaging from simple reconstruction to robust restoration by proposing a novel network architecture and a large-scale benchmark to effectively recover pristine scenes from real-world degraded measurements caused by motion blur and low light.

Hao Wang, Yuanfan Li, Qi Zhou, Zhankuo Xu, Jiong Ni, Xin Yuan2026-03-10💻 cs