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MCML

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Research library6linked papers
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Selected work

Representative Papers

Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos

Aug 17, 2026

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

Aug 14, 2026

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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Latest Papers

Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos

Aug 17, 2026

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

Aug 14, 2026

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