Search Results for author:
Found 18 papers, 7 papers with code
Enhancing Vision-Language Pre-training with Rich Supervisions
We propose Strongly Supervised pre-training with ScreenShots (S4) - a novel pre-training paradigm for Vision-Language Models using data from large-scale web screenshot rendering.
Multiple-Question Multiple-Answer Text-VQA
We present Multiple-Question Multiple-Answer (MQMA), a novel approach to do text-VQA in encoder-decoder transformer models.
DEED: Dynamic Early Exit on Decoder for Accelerating Encoder-Decoder Transformer Models
Based on the multi-exit model, we perform step-level dynamic early exit during inference, where the model may decide to use fewer decoder layers based on its confidence of the current layer at each individual decoding step.
A Multi-Modal Multilingual Benchmark for Document Image Classification
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents.
DocFormerv2: Local Features for Document Understanding
We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU).
Ranked #9 on
Visual Question Answering (VQA)
on DocVQA test
(using extra training data)
document understanding
Optical Character Recognition (OCR)
+1
SimCon Loss with Multiple Views for Text Supervised Semantic Segmentation
Instead of purely relying on the alignment from the noisy data, this paper proposes a novel loss function termed SimCon, which accounts for intra-modal similarities to determine the appropriate set of positive samples to align.
YORO -- Lightweight End to End Visual Grounding
We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task.
MixGen: A New Multi-Modal Data Augmentation
Data augmentation is a necessity to enhance data efficiency in deep learning.
Towards Differential Relational Privacy and its use in Question Answering
While bounding general memorization can have detrimental effects on the performance of a trained model, bounding RM does not prevent effective learning.
LaTr: Layout-Aware Transformer for Scene-Text VQA
Accounting for this, we propose a single objective pre-training scheme that requires only text and spatial cues.
DocFormer: End-to-End Transformer for Document Understanding
DocFormer uses text, vision and spatial features and combines them using a novel multi-modal self-attention layer.
Ranked #3 on
Document Image Classification
on RVL-CDIP
Towards Good Practices in Self-supervised Representation Learning
Self-supervised representation learning has seen remarkable progress in the last few years.
Saliency Driven Perceptual Image Compression
The proposed compression model incorporates the salient regions and optimizes on the proposed perceptual similarity metric.
Unbiased Evaluation of Deep Metric Learning Algorithms
Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification.
Human Perceptual Evaluations for Image Compression
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 compression schemes (such as JPEG, JPEG2000 or BPG).
Deep Perceptual Compression
In several cases, the MS-SSIM for deep learned techniques is higher than say a conventional, non-deep learned codec such as JPEG-2000 or BPG.
Scalable Logo Recognition using Proxies
Logo recognition is a challenging problem as there is no clear definition of a logo and there are huge variations of logos, brands and re-training to cover every variation is impractical.
Image similarity using Deep CNN and Curriculum Learning
Image similarity involves fetching similar looking images given a reference image.
