Search Results for author:
Found 15 papers, 3 papers with code
Multi-Contrast MRI Segmentation Trained on Synthetic Images
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.
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.
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation
To alleviate this need, we propose a self-supervised training approach for learning few-shot segmentation models.
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.
Image Captioning with Unseen Objects
Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing.
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.
Learning Visually Consistent Label Embeddings for Zero-Shot Learning
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.
RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes
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.
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.
Zero-Shot Object Detection by Hybrid Region Embedding
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.
Ranked #7 on
Zero-Shot Object Detection
on PASCAL VOC'07
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names.
Re-evaluating Automatic Metrics for Image Captioning
The task of generating natural language descriptions from images has received a lot of attention in recent years.
Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
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
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.
