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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer
[Project-page] [arXiv] [Code] [Paper]
Most state-of-the-art instance segmentation methods rely on large amounts of pixel-precise ground-truth annotations for training, which are expensive to create. Interactive segmentation networks help generate such annotations based on an image and the corresponding user interactions such as clicks. Existing methods for this task can only process a single instance at a time and each user interaction requires a full forward pass through the entire deep network. We introduce a more efficient approach, called DynaMITe, in which we represent user interactions as spatio-temporal queries to a Transformer decoder with a potential to segment multiple object instances in a single iteration. Our architecture also alleviates any need to re-compute image features during refinement, and requires fewer interactions for segmenting multiple instances in a single image when compared to other methods. DynaMITe achieves state-of- the-art results on multiple existing interactive segmentation benchmarks, and also on the new multi-instance benchmark that we propose in this paper.
Video Future Frames Generation using deep Encoder-decoder based Hierarchical Network
[Code] [Report]
Video frames generation is a challenging task due to the wide uncertainty in the nature of the problem. In this lab project, we approach the task of the video prediction using the model discussed in the lab, based on the Video Ladder Network. In our work, the moving Moving MNIST (MMNIST) and the KTH Action dataset are being used to perform the experiments. We present the effects of various design choices in the model architectures and the training settings. The final results achieved on both datasets are realistic and coherent with the given context frames, indicating the strong learning capability of the network.
Effect of Biased Training Data on Loss Landscape of Deep Neural Newworks
[Code] [Report]
Many studies show a positive correlation between the generalization ability of a deep neural network and the flatness of the minima in its loss landscape. Inspired by this statement, many studies investigate the effect of using different training pa- rameters and network architecture on the loss landscape of the neural network. This study investigates the effect of training a deep neural network on a biased dataset on its loss landscape by visualizing the loss landscape of the trained model. We found that different types of biases in the training dataset can affect the geometry of the loss landscape around the minima.
projects
DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer
[Project-page] [arXiv] [Code] [Paper]
Most state-of-the-art instance segmentation methods rely on large amounts of pixel-precise ground-truth annotations for training, which are expensive to create. Interactive segmentation networks help generate such annotations based on an image and the corresponding user interactions such as clicks. Existing methods for this task can only process a single instance at a time and each user interaction requires a full forward pass through the entire deep network. We introduce a more efficient approach, called DynaMITe, in which we represent user interactions as spatio-temporal queries to a Transformer decoder with a potential to segment multiple object instances in a single iteration. Our architecture also alleviates any need to re-compute image features during refinement, and requires fewer interactions for segmenting multiple instances in a single image when compared to other methods. DynaMITe achieves state-of- the-art results on multiple existing interactive segmentation benchmarks, and also on the new multi-instance benchmark that we propose in this paper.
Video Future Frames Generation using deep Encoder-decoder based Hierarchical Network
[Code] [Report]
Video frames generation is a challenging task due to the wide uncertainty in the nature of the problem. In this lab project, we approach the task of the video prediction using the model discussed in the lab, based on the Video Ladder Network. In our work, the moving Moving MNIST (MMNIST) and the KTH Action dataset are being used to perform the experiments. We present the effects of various design choices in the model architectures and the training settings. The final results achieved on both datasets are realistic and coherent with the given context frames, indicating the strong learning capability of the network.
Effect of Biased Training Data on Loss Landscape of Deep Neural Newworks
[Code] [Report]
Many studies show a positive correlation between the generalization ability of a deep neural network and the flatness of the minima in its loss landscape. Inspired by this statement, many studies investigate the effect of using different training pa- rameters and network architecture on the loss landscape of the neural network. This study investigates the effect of training a deep neural network on a biased dataset on its loss landscape by visualizing the loss landscape of the trained model. We found that different types of biases in the training dataset can affect the geometry of the loss landscape around the minima.
publications
DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer
[Project-page] [arXiv] [Code] [Paper]
Most state-of-the-art instance segmentation methods rely on large amounts of pixel-precise ground-truth annotations for training, which are expensive to create. Interactive segmentation networks help generate such annotations based on an image and the corresponding user interactions such as clicks. Existing methods for this task can only process a single instance at a time and each user interaction requires a full forward pass through the entire deep network. We introduce a more efficient approach, called DynaMITe, in which we represent user interactions as spatio-temporal queries to a Transformer decoder with a potential to segment multiple object instances in a single iteration. Our architecture also alleviates any need to re-compute image features during refinement, and requires fewer interactions for segmenting multiple instances in a single image when compared to other methods. DynaMITe achieves state-of- the-art results on multiple existing interactive segmentation benchmarks, and also on the new multi-instance benchmark that we propose in this paper.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.