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Found 12 papers, 2 papers with code
Hierarchical Prompts for Rehearsal-free Continual Learning
Nonetheless, these learnable prompts tend to concentrate on the discriminatory knowledge of the current task while ignoring past task knowledge, leading to that learnable prompts still suffering from catastrophic forgetting.
Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition
We introduce Hierarchical Augmentation and Distillation (HAD), which comprises the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM) to efficiently utilize the hierarchical structure of data and models, respectively.
Learning Differentially Private Probabilistic Models for Privacy-Preserving Image Generation
In particular, we first train a model to fit the distribution of the training data and make it satisfy differential privacy by performing a randomized response mechanism during training process.
LayoutDM: Transformer-based Diffusion Model for Layout Generation
Though existing methods based on generative models such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) have progressed, they still leave much room for improving the quality and diversity of the results.
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.
