Doppio: A Dataset for Contactless Weight Estimation of Falling Particles
研究通过计算机视觉和深度学习方法，利用Doppio数据集解决了工业应用中粉末状落料无接触重量估计的问题。
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Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos
This study addresses the limitations of existing process planning methods, which often lack task logic constraints and struggle to exclude irrelevant actions. To overcome these challenges, we propose CEFITO, a novel approach that learns an action-conditioned representation space to reframe process planning as a test-time task-constrained optimization problem. By leveraging contrastive energy fields, CEFITO explicitly eliminates irrelevant actions, ensuring generated sequences adhere to logical constraints. Experimental results demonstrate that CEFITO achieves state-of-the-art accuracy on two mainstream benchmarks, significantly enhancing both logical consistency and dynamic adaptability in planning. This work establishes a new paradigm for complex task planning by integrating explicit constraint satisfaction into the generation process.
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MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection
This study addresses the poor cross-domain generalization in monocular temporal 3D detection caused by learnable query overfitting. We propose the first domain generalization method for this task, introducing a Domain-Robust Anchor Generator (DRAG) and Temporal Refinement Identity Merging (TRIM) strategy to effectively decouple spatial distribution dependencies and optimize temporal associations. Furthermore, we establish a comprehensive cross-dataset generalization benchmark. Experimental results demonstrate that our approach improves zero-shot cross-domain NDS from 12.1% to 18.6% while simultaneously enhancing in-domain accuracy. By comprehensively outperforming existing baselines, this work significantly strengthens model robustness and generalization capabilities across diverse domains.
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Doppio: A Dataset for Contactless Weight Estimation of Falling Particles
研究通过计算机视觉和深度学习方法，利用Doppio数据集解决了工业应用中粉末状落料无接触重量估计的问题。
0 citationsRead paper
Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos
This study addresses the limitations of existing process planning methods, which often lack task logic constraints and struggle to exclude irrelevant actions. To overcome these challenges, we propose CEFITO, a novel approach that learns an action-conditioned representation space to reframe process planning as a test-time task-constrained optimization problem. By leveraging contrastive energy fields, CEFITO explicitly eliminates irrelevant actions, ensuring generated sequences adhere to logical constraints. Experimental results demonstrate that CEFITO achieves state-of-the-art accuracy on two mainstream benchmarks, significantly enhancing both logical consistency and dynamic adaptability in planning. This work establishes a new paradigm for complex task planning by integrating explicit constraint satisfaction into the generation process.
0 citationsRead paper
MAGneT-3D: Monocular and Domain-Generalizable Temporal 3D Detection
This study addresses the poor cross-domain generalization in monocular temporal 3D detection caused by learnable query overfitting. We propose the first domain generalization method for this task, introducing a Domain-Robust Anchor Generator (DRAG) and Temporal Refinement Identity Merging (TRIM) strategy to effectively decouple spatial distribution dependencies and optimize temporal associations. Furthermore, we establish a comprehensive cross-dataset generalization benchmark. Experimental results demonstrate that our approach improves zero-shot cross-domain NDS from 12.1% to 18.6% while simultaneously enhancing in-domain accuracy. By comprehensively outperforming existing baselines, this work significantly strengthens model robustness and generalization capabilities across diverse domains.
0 citationsRead paper