activity
20162023
most citedLearning What and Where to Draw

210 citations · 633 across the 54 of their papers we have counts for

collaborators
Showing 2020Show all

8 papers · 1 filter

cs.CV2020

Towards Recognizing Unseen Categories in Unseen Domains

Massimiliano Mancini, Zeynep Akata, Elisa Ricci +1

Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core…

cs.CV2020

Attribute Prototype Network for Zero-Shot Learning

Wenjia Xu, Yongqin Xian, Jiuniu Wang +2

From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to…

cs.CV2020

Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets

Junsuk Choe, Seong Joon Oh, Sanghyuk Chun +3

Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal…

cs.CV2020★ 4 cited

Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring

Yao Rong, Zeynep Akata, Enkelejda Kasneci

Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable if such driving maneuvers are detected beforehand and the driver is assisted…

cs.CV2020

Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-based Image Retrieval

Anjan Dutta, Zeynep Akata

Low-shot sketch-based image retrieval is an emerging task in computer vision, allowing to retrieve natural images relevant to hand-drawn sketch queries that are rarely seen during…

cs.CL2020

e-SNLI-VE: Corrected Visual-Textual Entailment with Natural Language Explanations

Virginie Do, Oana-Maria Camburu, Zeynep Akata +1

The recently proposed SNLI-VE corpus for recognising visual-textual entailment is a large, real-world dataset for fine-grained multimodal reasoning. However, the automatic way in w…