Jiaming Zhang

Affiliations

IAR-CVHCI, KIT

papers

495

total citations

papers (16)

Delivering Arbitrary-Modal Semantic Segmentation

CVPR 2023arXiv

156

citations

Bending Reality: Distortion-Aware Transformers for Adapting to Panoramic Semantic Segmentation

CVPR 2022arXiv

citations

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models

CVPR 2025arXiv

citations

Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

CVPR 2025arXiv

citations

RoDLA: Benchmarking the Robustness of Document Layout Analysis Models

CVPR 2024arXiv

citations

Occlusion-Aware Seamless Segmentation

ECCV 2024arXiv

citations

Scene-agnostic Pose Regression for Visual Localization

CVPR 2025arXiv

citations

Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation

ICCV 2025arXiv

citations

SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

CVPR 2025arXiv

citations

FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning

NEURIPS 2025arXiv

citations

One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment

ICML 2024

citations

Jiaming Zhang

Affiliations

papers (16)

Delivering Arbitrary-Modal Semantic Segmentation

Bending Reality: Distortion-Aware Transformers for Adapting to Panoramic Semantic Segmentation

Capturing Omni-Range Context for Omnidirectional Segmentation

Adversarial Prompt Tuning for Vision-Language Models

Unlearnable Clusters: Towards Label-Agnostic Unlearnable Examples

Navigating Open Set Scenarios for Skeleton-Based Action Recognition

Open Panoramic Segmentation

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models

Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

RoDLA: Benchmarking the Robustness of Document Layout Analysis Models

Occlusion-Aware Seamless Segmentation

Scene-agnostic Pose Regression for Visual Localization

Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation

SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning

One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment

papers (16)

Delivering Arbitrary-Modal Semantic Segmentation

Bending Reality: Distortion-Aware Transformers for Adapting to Panoramic Semantic Segmentation

Capturing Omni-Range Context for Omnidirectional Segmentation

Adversarial Prompt Tuning for Vision-Language Models

Unlearnable Clusters: Towards Label-Agnostic Unlearnable Examples

Navigating Open Set Scenarios for Skeleton-Based Action Recognition

Open Panoramic Segmentation

TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models

Anyattack: Towards Large-scale Self-supervised Adversarial Attacks on Vision-language Models

RoDLA: Benchmarking the Robustness of Document Layout Analysis Models

Occlusion-Aware Seamless Segmentation

Scene-agnostic Pose Regression for Visual Localization

Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation

SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning

One for All: A Universal Generator for Concept Unlearnability via Multi-Modal Alignment