15 citations · 40 across the 5 of their papers we have counts for
7 papers
Video Generation from Text Employing Latent Path Construction for Temporal Modeling
Amir Mazaheri, Mubarak Shah
Video generation is one of the most challenging tasks in Machine Learning and Computer Vision fields of study. In this paper, we tackle the text to video generation problem, which…
MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering
Aisha Urooj Khan, Amir Mazaheri, Niels da Vitoria Lobo +1
We present MMFT-BERT(MultiModal Fusion Transformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input moda…
Deep Photo Cropper and Enhancer
Aaron Ott, Amir Mazaheri, Niels D. Lobo +1
This paper introduces a new type of image enhancement problem. Compared to traditional image enhancement methods, which mostly deal with pixel-wise modifications of a given photo,…
Pay attention! - Robustifying a Deep Visuomotor Policy through Task-Focused Attention
Pooya Abolghasemi, Amir Mazaheri, Mubarak Shah +1
Several recent studies have demonstrated the promise of deep visuomotor policies for robot manipulator control. Despite impressive progress, these systems are known to be vulnerabl…
Visual Text Correction
Amir Mazaheri, Mubarak Shah
Videos, images, and sentences are mediums that can express the same semantics. One can imagine a picture by reading a sentence or can describe a scene with some words. However, eve…
Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal Attentions
Amir Mazaheri, Dong Zhang, Mubarak Shah
Given a video and a description sentence with one missing word (we call it the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the missing word automatically.…
Video Fill in the Blank with Merging LSTMs
Amir Mazaheri, Dong Zhang, Mubarak Shah
Given a video and its incomplete textural description with missing words, the Video-Fill-in-the-Blank (ViFitB) task is to automatically find the missing word. The contextual inform…