Search Results for author:
Found 6 papers, 2 papers with code
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA
In particular, our approach improves the accuracy of the previous state-of-the-art approach from 38% to 54% on the human-written questions in the ChartQA dataset, which needs strong reasoning.
YORO -- Lightweight End to End Visual Grounding
We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task.
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
Skeleton based Zero Shot Action Recognition in Joint Pose-Language Semantic Space
In this work, we present a body pose based zero shot action recognition network and demonstrate its performance on the NTU RGB-D dataset.
Are we asking the right questions in MovieQA?
Joint vision and language tasks like visual question answering are fascinating because they explore high-level understanding, but at the same time, can be more prone to language biases.
Learning Sampling Policies for Domain Adaptation
We address the problem of semi-supervised domain adaptation of classification algorithms through deep Q-learning.
