Search Results for author:
Found 18 papers, 9 papers with code
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains.
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
Revisiting Paraphrase Question Generator using Pairwise Discriminator
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
SpectFormer: Frequency and Attention is what you need in a Vision Transformer
Vision transformers have been applied successfully for image recognition tasks.
Efficiency 360: Efficient Vision Transformers
Transformers are widely used for solving tasks in natural language processing, computer vision, speech, and music domains.
Multimodal Differential Network for Visual Question Generation
Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations.
Robust Explanations for Visual Question Answering
Our model explains the answers obtained through a VQA model by providing visual and textual explanations.
Barlow constrained optimization for Visual Question Answering
In this paper, we propose a novel regularization for VQA models, Constrained Optimization using Barlow's theory (COB), that improves the information content of the joint space by minimizing the redundancy.
Spectral Convolutional Transformer: Harmonizing Real vs. Complex Multi-View Spectral Operators for Vision Transformer
Transformers used in vision have been investigated through diverse architectures - ViT, PVT, and Swin.
U-CAM: Visual Explanation using Uncertainty based Class Activation Maps
These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions.
Probabilistic framework for solving Visual Dialog
In this paper, we propose a probabilistic framework for solving the task of `Visual Dialog'.
Ranked #1 on
Common Sense Reasoning
on Visual Dialog v0.9
Dynamic Attention Networks for Task Oriented Grounding
In this work, we show that Dynamic Attention helps in achieving grounding and also aids in the policy learning objective.
Granular Multimodal Attention Networks for Visual Dialog
Particularly, in this work, we propose a new method Granular Multi-modal Attention, where we aim to particularly address the question of the right granularity at which one needs to attend while solving the Visual Dialog task.
Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA
It also results in a good improvement in rank correlation metric on the VQA task.
Deep Exemplar Networks for VQA and VQG
Specifically, we incorporate exemplar based approaches and show that an exemplar based module can be incorporated in almost any of the deep learning architectures proposed in the literature and the addition of such a block results in improved performance for solving these tasks.
Deep Bayesian Network for Visual Question Generation
This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study.
Uncertainty based Class Activation Maps for Visual Question Answering
These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions.
Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting
We define distillation losses in terms of aleatoric uncertainty and self-attention.
