Search Results for author:
Found 9 papers, 4 papers with code
Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory
Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain.
Part-Guided Attention Learning for Vehicle Instance Retrieval
Finally, we aggregate the global appearance and part features to improve the feature performance further.
End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end.
Ranked #1 on
6D Pose Estimation using RGB
on LineMOD
(Accuracy metric)
Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement
We propose an approach to estimate the 6DOF pose of a satellite, relative to a canonical pose, from a single image.
Self-training with progressive augmentation for unsupervised cross-domain person re-identification
In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset.
Ranked #11 on
Unsupervised Domain Adaptation
on Market to Duke
V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices
One of the primary challenges faced by deep learning is the degree to which current methods exploit superficial statistics and dataset bias, rather than learning to generalise over the specific representations they have experienced.
Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks
Being composed of node attention component and edge attention component, the proposed graph attention mechanism explicitly represents inter-object relationships, and properties with a flexibility and power impossible with competing approaches.
Leveraging Weak Semantic Relevance for Complex Video Event Classification
In this paper, we propose a novel approach to automatically maximize the utility of weak semantic annotations (formalized as the semantic relevance of video shots to the target event) to facilitate video event classification.
Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
Instance retrieval requires one to search for images that contain a particular object within a large corpus.
