| 1 |
A Unified Framework for 3D Scene Understanding |
UniSeg3D:提出统一的3D场景理解框架,实现多任务分割并超越SOTA方法。 |
contrastive learning distillation scene understanding |
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| 2 |
ACTRESS: Active Retraining for Semi-supervised Visual Grounding |
ACTRESS:面向半监督视觉定位的主动重训练方法 |
teacher-student visual grounding |
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| 3 |
FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning |
FlowCon:结合流模型与对比学习的分布外数据检测方法 |
representation learning contrastive learning |
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| 4 |
BVI-RLV: A Fully Registered Dataset and Benchmarks for Low-Light Video Enhancement |
提出BVI-RLV数据集,用于低光视频增强的训练和基准测试 |
Mamba state space model spatiotemporal |
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| 5 |
Lift, Splat, Map: Lifting Foundation Masks for Label-Free Semantic Scene Completion |
LSMap:利用视觉基础模型进行无标签语义场景补全,提升城市场景感知能力。 |
representation learning foundation model |
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| 6 |
Cyclic Refiner: Object-Aware Temporal Representation Learning for Multi-View 3D Detection and Tracking |
提出循环精炼器,用于多视角3D检测与跟踪中的目标感知时序表征学习。 |
representation learning |
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| 7 |
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation |
提出基于无监督知识蒸馏的提示学习方法,提升视觉-语言模型零样本泛化能力 |
distillation |
✅ |
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| 8 |
Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation |
提出基于增强FCOS-Lite和知识蒸馏的边缘AI鸡群健康检测方案 |
distillation |
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| 9 |
Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization |
提出基于知识蒸馏和量化的边缘设备统一异常检测方法 |
distillation |
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