210 citations · 633 across the 54 of their papers we have counts for
26 papers · 1 filter
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation
Anurag Das, Yongqin Xian, Yang He +2
For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets. In this work, we show that coarse anno…
Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment
Yuchen Ma, Yanbei Chen, Zeynep Akata
Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised…
Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning
Zafir Stojanovski, Karsten Roth, Zeynep Akata
Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts.…
Cross-Modal Fusion Distillation for Fine-Grained Sketch-Based Image Retrieval
Abhra Chaudhuri, Massimiliano Mancini, Yanbei Chen +2
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information. As instances from different moda…
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
Abhra Chaudhuri, Massimiliano Mancini, Zeynep Akata +1
Fine-grained categories that largely share the same set of parts cannot be discriminated based on part information alone, as they mostly differ in the way the local parts relate to…
Disentanglement of Correlated Factors via Hausdorff Factorized Support
Karsten Roth, Mark Ibrahim, Zeynep Akata +2
A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning…