60 citations · 134 across the 13 of their papers we have counts for
14 papers
DocFormerv2: Local Features for Document Understanding
Srikar Appalaraju, Peng Tang, Qi Dong +3
We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU). The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extrac…
SimCon Loss with Multiple Views for Text Supervised Semantic Segmentation
Yash Patel, Yusheng Xie, Yi Zhu +2
Learning to segment images purely by relying on the image-text alignment from web data can lead to sub-optimal performance due to noise in the data. The noise comes from the sample…
YORO -- Lightweight End to End Visual Grounding
Chih-Hui Ho, Srikar Appalaraju, Bhavan Jasani +2
We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task. This task involves localizing, in an image, an object referred via natural…
MixGen: A New Multi-Modal Data Augmentation
Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju +4
Data augmentation is a necessity to enhance data efficiency in deep learning. For vision-language pre-training, data is only augmented either for images or for text in previous wor…
Towards Differential Relational Privacy and its use in Question Answering
Simone Bombari, Alessandro Achille, Zijian Wang +6
Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) t…
LaTr: Layout-Aware Transformer for Scene-Text VQA
Ali Furkan Biten, Ron Litman, Yusheng Xie +2
We propose a novel multimodal architecture for Scene Text Visual Question Answering (STVQA), named Layout-Aware Transformer (LaTr). The task of STVQA requires models to reason over…
DocFormer: End-to-End Transformer for Document Understanding
Srikar Appalaraju, Bhavan Jasani, Bhargava Urala Kota +2
We present DocFormer -- a multi-modal transformer based architecture for the task of Visual Document Understanding (VDU). VDU is a challenging problem which aims to understand docu…
Towards Good Practices in Self-supervised Representation Learning
Srikar Appalaraju, Yi Zhu, Yusheng Xie +1
Self-supervised representation learning has seen remarkable progress in the last few years. More recently, contrastive instance learning has shown impressive results compared to it…
Saliency Driven Perceptual Image Compression
Yash Patel, Srikar Appalaraju, R. Manmatha
This paper proposes a new end-to-end trainable model for lossy image compression, which includes several novel components. The method incorporates 1) an adequate perceptual similar…
Unbiased Evaluation of Deep Metric Learning Algorithms
Istvan Fehervari, Avinash Ravichandran, Srikar Appalaraju
Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common…
Human Perceptual Evaluations for Image Compression
Yash Patel, Srikar Appalaraju, R. Manmatha
Recently, there has been much interest in deep learning techniques to do image compression and there have been claims that several of these produce better results than engineered c…
Deep Perceptual Compression
Yash Patel, Srikar Appalaraju, R. Manmatha
Several deep learned lossy compression techniques have been proposed in the recent literature. Most of these are optimized by using either MS-SSIM (multi-scale structural similarit…
Scalable Logo Recognition using Proxies
Istvan Fehervari, Srikar Appalaraju
Logo recognition is the task of identifying and classifying logos. Logo recognition is a challenging problem as there is no clear definition of a logo and there are huge variations…
Image similarity using Deep CNN and Curriculum Learning
Srikar Appalaraju, Vineet Chaoji
Image similarity involves fetching similar looking images given a reference image. Our solution called SimNet, is a deep siamese network which is trained on pairs of positive and n…