Publications/Reports
We formulate a new structured framework for dense video understanding and propose a Transformer based model, VideoWhisperer that operates on a group of clips and jointly predicts all the salient actions, Semantic roles via captioning and, spatio temporal grounding in a weakly supervised setting
Zeeshan Khan, C.V. Jawahar, Makarand Tapaswi
In Neural Information Processing Systems (NeurIPS), 2022
We propose to recursively prune and retrain a Transformer to find language dependent submodules that involves 2 type of paramteres, 1)Shared multlingual and 2)Unique Language dependent parameters, to overcome negative interference in Multilingual Neural Machine translation.
Zeeshan Khan, Kartheek Akella, Vinay Namboodiri, and C.V. Jawahar
In Association For Computational Linguistics (ACL) (Findings), 2021
This is the first attempt towards generating high speed high dynamic range videos from low speed low dynamic range videos. We use video frame interpolation to recursivrly generate the high and low exposure images missing in the input alternative exposure frames. The High and Low exposure frames are merged at each timestep to generate an HDR video.
Zeeshan Khan, Parth Shettiwar, Mukul Khanna, Shanmuganathan Raman
In International Conference on Pattern Recognition(ICPR), 2022 (ORAL)
We present a robut deep architecture for Appearance Consistent person image generation in novel poses. We incorporate a 3 stream network, for image, pose, and appearance. Additionaly we use Gated convolutions and, Non-local attention blocks for generating realistic images.
Ashish Tiwari, Zeeshan Khan, Shanmuganathan Raman
In International Conference on Pattern Recognition (ICPR), 2022
We address the task of improving pair-wise machine translation for low resource Indian languages using a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora
Kartheek Akella, Sai Himal Allu, Sridhar Suresh Ragupathi, Aman Singhal,Zeeshan Khan, Vinay Namboodiri, and C.V. Jawahar
In International Conference on Natural Language Processing(ICON) 2020
Proposed a recurrent Feedback CNN for HDR image reconstruction from a single exposure LDR image, achieving SOTA results on all the HDR benchmarks. Designed a novel Dense Feedback Block using hidden states of RNN, to transfer the high-level information to the low-level features. LDR to HDR representations are learned in multiple iterations via feedback loops.
Zeeshan Khan, Mukul khanna, and Prof. Shanmuganathan Raman
In Global Conference on Signal and Information Processing (GlobalSIP) 2019 (ORAL)