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Towards Zero-shot Sign Language Recognition
For this novel problem setup, we introduce three benchmark datasets with their accompanying textual and attribute descriptions to analyze the problem in detail.
Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos
To mimic such scenarios, we formulate a realistic domain-transfer problem, where the goal is to transfer the recognition model trained on clean posed images to the target domain of violent videos, where training videos are available only for a subset of subjects.
Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?
We introduce the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign class examples to recognize the instances of unseen signs.
Wildest Faces: Face Detection and Recognition in Violent Settings
With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks.
