22 citations · 60 across the 5 of their papers we have counts for
6 papers
PlanT: Explainable Planning Transformers via Object-Level Representations
Katrin Renz, Kashyap Chitta, Otniel-Bogdan Mercea +3
Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene. While human drivers prioritize important objects and ignore details not…
Human Attention in Fine-grained Classification
Yao Rong, Wenjia Xu, Zeynep Akata +1
The way humans attend to, process and classify a given image has the potential to vastly benefit the performance of deep learning models. Exploiting where humans are focusing can r…
Robustness via Uncertainty-aware Cycle Consistency
Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs. Existing methods learn deterministic mappings without explicitl…
Fine-Grained Zero-Shot Learning with DNA as Side Information
Sarkhan Badirli, Zeynep Akata, George Mohler +2
Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes. As manually annotated visual attribu…
Distilling Audio-Visual Knowledge by Compositional Contrastive Learning
Yanbei Chen, Yongqin Xian, A. Sophia Koepke +2
Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to t…
Prototype-based Incremental Few-Shot Semantic Segmentation
Fabio Cermelli, Massimiliano Mancini, Yongqin Xian +2
Semantic segmentation models have two fundamental weaknesses: i) they require large training sets with costly pixel-level annotations, and ii) they have a static output space, cons…