Search Results for author:
Found 8 papers, 1 papers with code
ImbaGCD: Imbalanced Generalized Category Discovery
Therefore, we present a challenging and practical problem, Imbalanced Generalized Category Discovery (ImbaGCD), where the distribution of unlabeled data is imbalanced, with known classes being more frequent than unknown ones.
Generalized Categories Discovery for Long-tailed Recognition
Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set comprising recognized classes.
Supervised Knowledge May Hurt Novel Class Discovery Performance
Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance.
A Closer Look at Novel Class Discovery from the Labeled Set
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes.
Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications
Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse.
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints.
Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation
Augmented Reality/Virtual Reality (AR/VR) glasses are widely foreseen as the next generation computing platform.
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework.
