Search Results for author:
Found 9 papers, 3 papers with code
A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting.
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection
We argue that this contextual information should already be embedded within the known classes.
Cryo-shift: Reducing domain shift in cryo-electron subtomograms with unsupervised domain adaptation and randomization
Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that enables the visualization of subcellular structures in situ at near-atomic resolution.
Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer
In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks.
Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations
We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images.
Active Learning in Video Tracking
We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos.
Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multiclass classification.
Efficient and Consistent Adversarial Bipartite Matching
Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations.
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection
We aim to find an optimal adversarial perturbations of the ground truth data (i. e., the worst case perturbations) that forces the object bounding box predictor to learn from the hardest distribution of perturbed examples for better test-time performance.
