Search Results for author: Nazli Ikizler-Cinbis

Found 15 papers, 3 papers with code

Multi-Contrast MRI Segmentation Trained on Synthetic Images

no code implementations6 Jul 2022 Ismail Irmakci, Zeki Emre Unel, Nazli Ikizler-Cinbis, Ulas Bagci

Based on synthetic image training, our segmentation results were as high as 93. 91\%, 94. 11\%, 91. 63\%, 95. 33\%, for muscle, fat, bone, and bone marrow delineation, respectively.

Image Segmentation MRI segmentation +2

Towards Zero-shot Sign Language Recognition

no code implementations15 Jan 2022 Yunus Can Bilge, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis

For this novel problem setup, we introduce three benchmark datasets with their accompanying textual and attribute descriptions to analyze the problem in detail.

Attribute Descriptive +3

Red Carpet to Fight Club: Partially-supervised Domain Transfer for Face Recognition in Violent Videos

no code implementations16 Sep 2020 Yunus Can Bilge, Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis, Pinar Duygulu

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.

Face Recognition

Image Captioning with Unseen Objects

no code implementations31 Jul 2019 Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis

Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing.

Caption Generation Image Captioning +6

Zero-Shot Sign Language Recognition: Can Textual Data Uncover Sign Languages?

no code implementations24 Jul 2019 Yunus Can Bilge, Nazli Ikizler-Cinbis, Ramazan Gokberk Cinbis

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.

Descriptive Object Recognition +3

Learning Visually Consistent Label Embeddings for Zero-Shot Learning

no code implementations16 May 2019 Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis

In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner.

Transfer Learning Zero-Shot Learning

RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes

no code implementations EMNLP 2018 Semih Yagcioglu, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis

With over 36K automatically generated question-answer pairs, we design a set of comprehension and reasoning tasks that require joint understanding of images and text, capturing the temporal flow of events and making sense of procedural knowledge.

Reading Comprehension

Wildest Faces: Face Detection and Recognition in Violent Settings

no code implementations19 May 2018 Mehmet Kerim Yucel, Yunus Can Bilge, Oguzhan Oguz, Nazli Ikizler-Cinbis, Pinar Duygulu, Ramazan Gokberk Cinbis

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.

Face Detection Face Recognition

Zero-Shot Object Detection by Hybrid Region Embedding

2 code implementations16 May 2018 Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis

Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images.

Object object-detection +1

Re-evaluating Automatic Metrics for Image Captioning

no code implementations EACL 2017 Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, Erkut Erdem

The task of generating natural language descriptions from images has received a lot of attention in recent years.

Image Captioning

Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures

no code implementations15 Jan 2016 Raffaella Bernardi, Ruket Cakici, Desmond Elliott, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis, Frank Keller, Adrian Muscat, Barbara Plank

Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities.


Facial Descriptors for Human Interaction Recognition In Still Images

no code implementations17 Sep 2015 Gokhan Tanisik, Cemil Zalluhoglu, Nazli Ikizler-Cinbis

Our designed facial descriptors are based on the observation that relative positions, size and locations of the faces are likely to be important for characterizing human interactions.

Human Interaction Recognition

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