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Found 8 papers, 1 papers with code
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology
We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets.
Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology
Our Additive MIL models enable spatial credit assignment such that the contribution of each region in the image can be exactly computed and visualized.
Rethinking Machine Learning Model Evaluation in Pathology
Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes.
CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems
Autonomous camera systems are often subjected to an optimization/filtering operation to smoothen and stabilize the rough trajectory estimates.
Embodied Language Grounding with 3D Visual Feature Representations
We propose associating language utterances to 3D visual abstractions of the scene they describe.
MergeNet: A Deep Net Architecture for Small Obstacle Discovery
We present here, a novel network architecture called MergeNet for discovering small obstacles for on-road scenes in the context of autonomous driving.
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision
Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities.
Object-Level Context Modeling For Scene Classification with Context-CNN
Convolutional Neural Networks (CNNs) have been used extensively for computer vision tasks and produce rich feature representation for objects or parts of an image.
