Search Results for author:
Found 9 papers, 6 papers with code
Multi-granularity Correspondence Learning from Long-term Noisy Videos
Existing video-language studies mainly focus on learning short video clips, leaving long-term temporal dependencies rarely explored due to over-high computational cost of modeling long videos.
Ranked #1 on
Zero-Shot Video Retrieval
on YouCook2
Action Segmentation
Long Video Retrieval (Background Removed)
+2
Robust Domain Adaptation for Machine Reading Comprehension
Such a process will inevitably introduce mismatched pairs (i. e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain.
Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification
To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART).
Learning with Noisy Correspondence for Cross-modal Matching
Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
Partially View-aligned Clustering
To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC).
Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation
Power system dynamic state estimation (DSE) remains an active research area.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Power system emergency control is generally regarded as the last safety net for grid security and resiliency.
