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Found 8 papers, 2 papers with code
Estimation of Reliable Proposal Quality for Temporal Action Detection
For the region perspective, we introduce Region Evaluate Module (REM) which uses a new and efficient sampling method for proposal feature representation containing more contextual information compared with point feature to refine category score and proposal boundary.
One-shot Key Information Extraction from Document with Deep Partial Graph Matching
However, collecting and labeling a large dataset is time-consuming and is not a user-friendly requirement for many cloud platforms.
Soft Hindsight Experience Replay
However, due to the brittleness of deterministic methods, HER and its variants typically suffer from a major challenge for stability and convergence, which significantly affects the final performance.
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution
Results show that our model can outperform the dominant models consistently in these tasks.
Graph Construction with Label Information for Semi-Supervised Learning
In the literature, most existing graph-based semi-supervised learning (SSL) methods only use the label information of observed samples in the label propagation stage, while ignoring such valuable information when learning the graph.
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.
Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment
In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class.
Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced.
