Search Results for author:
Found 20 papers, 6 papers with code
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training
We demonstrate the effectiveness of our approach, named LOCATE, on multiple standard video object segmentation, image saliency detection, and object segmentation benchmarks, achieving results on par with and, in many cases surpassing state-of-the-art methods.
FODVid: Flow-guided Object Discovery in Videos
Segmentation of objects in a video is challenging due to the nuances such as motion blurring, parallax, occlusions, changes in illumination, etc.
SARC: Soft Actor Retrospective Critic
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual consistency between the two.
Parameter Efficient Local Implicit Image Function Network for Face Segmentation
Face parsing is defined as the per-pixel labeling of images containing human faces.
Towards Estimating Transferability using Hard Subsets
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensive fine tuning.
UMFuse: Unified Multi View Fusion for Human Editing applications
Numerous pose-guided human editing methods have been explored by the vision community due to their extensive practical applications.
VGFlow: Visibility guided Flow Network for Human Reposing
The task of human reposing involves generating a realistic image of a person standing in an arbitrary conceivable pose.
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents.
Video2Skill: Adapting Events in Demonstration Videos to Skills in an Environment using Cyclic MDP Homomorphisms
Humans excel at learning long-horizon tasks from demonstrations augmented with textual commentary, as evidenced by the burgeoning popularity of tutorial videos online.
Form2Seq : A Framework for Higher-Order Form Structure Extraction
To mitigate this, we propose Form2Seq, a novel sequence-to-sequence (Seq2Seq) inspired framework for structure extraction using text, with a specific focus on forms, which leverages relative spatial arrangement of structures.
Multi-Modal Association based Grouping for Form Structure Extraction
Experimental results show the effectiveness of our approach achieving a recall of 90. 29%, 73. 80%, 83. 12%, and 52. 72% for the above structures, respectively, outperforming semantic segmentation baselines significantly.
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Also, they are believed to be arranged hierarchically, allowing for an efficient representation of complex long-horizon experiences.
SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval
The ability to efficiently search for images is essential for improving the user experiences across various products.
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains.
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss
We show how agents trained with SQLoss evolve cooperative behavior in several social dilemma matrix games.
Document Structure Extraction using Prior based High Resolution Hierarchical Semantic Segmentation
We introduce our new human-annotated forms dataset and show that our method significantly outperforms different segmentation baselines on this dataset in extracting hierarchical structures.
Retrospection: Leveraging the Past for Efficient Training of Deep Neural Networks
Deep neural networks are powerful learning machines that have enabled breakthroughs in several domains.
Attention Based Natural Language Grounding by Navigating Virtual Environment
We develop an attention mechanism for multi-modal fusion of visual and textual modalities that allows the agent to learn to complete the task and achieve language grounding.
Learning to navigate by distilling visual information and natural language instructions
In this work, we focus on the problem of grounding language by training an agent to follow a set of natural language instructions and navigate to a target object in a 2D grid environment.
Introspection: Accelerating Neural Network Training By Learning Weight Evolution
In this paper, we explore the idea of learning weight evolution pattern from a simple network for accelerating training of novel neural networks.
