OVRSISBenchV2 is a realistic benchmark expanding scene and category coverage for open-vocabulary remote sensing segmentation, with Pi-Seg baseline showing strong transfer via positive-incentive noise perturbations.
arXiv preprint arXiv:2510.15398 (2025)
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IDCL adds density-based curriculum learning and density-core guidance to deep image clustering, claiming superior robustness, faster convergence, and flexibility on benchmark datasets.
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Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline
OVRSISBenchV2 is a realistic benchmark expanding scene and category coverage for open-vocabulary remote sensing segmentation, with Pi-Seg baseline showing strong transfer via positive-incentive noise perturbations.
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Deep Image Clustering Based on Curriculum Learning and Density Information
IDCL adds density-based curriculum learning and density-core guidance to deep image clustering, claiming superior robustness, faster convergence, and flexibility on benchmark datasets.
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The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery
EAGC mitigates gradient entanglement in GCD by anchoring supervised gradients and adaptively projecting unlabeled ones, boosting existing methods to new state-of-the-art performance.
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PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
PALM improves long-horizon robotic manipulation success by distilling affordance representations for object interaction and predicting within-subtask progress in a VLA model.
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The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
The NTIRE 2026 CD-FSOD Challenge report details innovative methods and performance results from 19 teams on cross-domain few-shot object detection in open- and closed-source tracks.