Introduces Targeted Downstream-Agnostic Attack (TDAA) that uses a threat image as feature anchor and example-specific perturbations to achieve targeted attacks on unknown downstream tasks from pre-trained encoders.
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UNVERDICTED 4representative citing papers
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
Combining semantic and geometric prompts with light fine-tuning gives the best SAM3 performance on remote sensing segmentation, while text-only prompting lags especially on irregular shapes.
citing papers explorer
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Targeted Downstream-Agnostic Attack
Introduces Targeted Downstream-Agnostic Attack (TDAA) that uses a threat image as feature anchor and example-specific perturbations to achieve targeted attacks on unknown downstream tasks from pre-trained encoders.
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Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
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Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
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On the Effectiveness of Textual Prompting with Lightweight Fine-Tuning for SAM3 Remote Sensing Segmentation
Combining semantic and geometric prompts with light fine-tuning gives the best SAM3 performance on remote sensing segmentation, while text-only prompting lags especially on irregular shapes.