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Found 11 papers, 6 papers with code
Differentially Private Video Activity Recognition
In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored.
Learning Instance-Specific Adaptation for Cross-Domain Segmentation
We propose a test-time adaptation method for cross-domain image segmentation.
Learning Representational Invariances for Data-Efficient Action Recognition
Data augmentation is a ubiquitous technique for improving image classification when labeled data is scarce.
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
DRG: Dual Relation Graph for Human-Object Interaction Detection
We tackle the challenging problem of human-object interaction (HOI) detection.
Ranked #26 on
Human-Object Interaction Detection
on V-COCO
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling
Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation.
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.
iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection
Our core idea is that the appearance of a person or an object instance contains informative cues on which relevant parts of an image to attend to for facilitating interaction prediction.
Ranked #2 on
Human-Object Interaction Detection
on Ambiguious-HOI
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
We propose a framework that learns a representation transferable across different domains and tasks in a data efficient manner.
Label Efficient Learning of Transferable Representations across Domains and Tasks
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner.
Learning to Generate Long-term Future via Hierarchical Prediction
To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions.
