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Found 7 papers, 5 papers with code
Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning
The problem lies in that each application and task may require different auxiliary loss function, especially when tasks are diverse and distinct.
Searching for Controllable Image Restoration Networks
Diverse user preferences over images have recently led to a great amount of interest in controlling the imagery effects for image restoration tasks.
Meta-Learning with Adaptive Hyperparameters
Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization.
Visual Tracking by TridentAlign and Context Embedding
However, extensive scale variations of the target object and distractor objects with similar categories have consistently posed challenges in visual tracking.
Scene-Adaptive Video Frame Interpolation via Meta-Learning
Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
Deep Meta Learning for Real-Time Target-Aware Visual Tracking
In this paper, we propose a novel on-line visual tracking framework based on the Siamese matching network and meta-learner network, which run at real-time speeds.
Real-time visual tracking by deep reinforced decision making
In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods.
