Search Results for author:
Found 6 papers, 1 papers with code
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training.
Open World DETR: Transformer based Open World Object Detection
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision.
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes.
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task.
White blood cell classification
Subsequently, after successfully separating the white blood cell from the blood smear image, a large number of nonlinear features including geometrical, color and texture features are extracted.
Shared control schematic for brain controlled vehicle based on fuzzy logic
Brain controlled vehicle refers to the vehicle that obtains control commands by analyzing the driver's EEG through Brain-Computer Interface (BCI).
Electroencephalogram (EEG)
Systems and Control
Human-Computer Interaction
