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Conditional De-Identification of 3D Magnetic Resonance Images
Lennart Alexander Van der Goten, Tobias Hepp, Zeynep Akata +1
Privacy protection of medical image data is challenging. Even if metadata is removed, brain scans are vulnerable to attacks that match renderings of the face to facial image databa…
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning
Anjan Dutta, Massimiliano Mancini, Zeynep Akata
Existing self-supervised learning methods learn representation by means of pretext tasks which are either (1) discriminating that explicitly specify which features should be separa…
Uncertainty-Guided Progressive GANs for Medical Image Translation
Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp +2
Image-to-image translation plays a vital role in tackling various medical imaging tasks such as attenuation correction, motion correction, undersampled reconstruction, and denoisin…
Keep CALM and Improve Visual Feature Attribution
Jae Myung Kim, Junsuk Choe, Zeynep Akata +1
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applicati…
Where and When: Space-Time Attention for Audio-Visual Explanations
Yanbei Chen, Thomas Hummel, A. Sophia Koepke +1
Explaining the decision of a multi-modal decision-maker requires to determine the evidence from both modalities. Recent advances in XAI provide explanations for models trained on s…
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski +4
Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL…
Audio Retrieval with Natural Language Queries
Andreea-Maria Oncescu, A. Sophia Koepke, João F. Henriques +2
We consider the task of retrieving audio using free-form natural language queries. To study this problem, which has received limited attention in the existing literature, we introd…
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian +1
Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions of state and object visual primitives seen during training. A problem with standard CZSL is the assump…
A Closer Look at Self-training for Zero-Label Semantic Segmentation
Giuseppe Pastore, Fabio Cermelli, Yongqin Xian +3
Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation…
Uncertainty-aware Generalized Adaptive CycleGAN
Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner. Existing methods often learn deterministic mappings without explicitly…
Learning Graph Embeddings for Compositional Zero-shot Learning
Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari +1
In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) i…
Open World Compositional Zero-Shot Learning
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian +1
Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen c…