14 citations · 48 across the 18 of their papers we have counts for
6 papers · 1 filter
Domain Adaptive Knowledge Distillation for Driving Scene Semantic Segmentation
Divya Kothandaraman, Athira Nambiar, Anurag Mittal
Practical autonomous driving systems face two crucial challenges: memory constraints and domain gap issues. In this paper, we present a novel approach to learn domain adaptive know…
MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis
Rahul Chakwate, Arulkumar Subramaniam, Anurag Mittal
Representation learning from 3D point clouds is challenging due to their inherent nature of permutation invariance and irregular distribution in space. Existing deep learning metho…
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution
Vikram Singh, Anurag Mittal
Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. However, it is yet to be fully explored in solving…
Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks
Gouthaman KV, Athira Nambiar, Kancheti Sai Srinivas +1
Attention models are widely used in Vision-language (V-L) tasks to perform the visual-textual correlation. Humans perform such a correlation with a strong linguistic understanding…
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder
Gouthaman KV, Anurag Mittal
Recent studies have shown that current VQA models are heavily biased on the language priors in the train set to answer the question, irrespective of the image. E.g., overwhelmingly…
Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval
Anubha Pandey, Ashish Mishra, Vinay Kumar Verma +2
Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training. The assumption may not always be practical sin…