SelectedWorks - Hisham Cholakkal

Selected Works of Hisham Cholakkal

Assistant Professor, Department of Computer Vision, MBZUAI

Dr. Hisham Cholakkal is an Assistant Professor in the Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
 
Biography

Dr. Hisham Cholakkal was a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi. He received PhD degree in Computer Vision from Nanyang Technological University, Singapore and M.Tech degree in Digital Signal Processing from Indian Institute of Technology Guwahati, India. He has six years of experience at various industrial and academic research labs. Before joining IIAI, he was a Senior Technical Lead in the Computer Vision and Deep learning Research team at Mercedes-Benz R&D, India. He has also worked as a computer vision researcher at BEL-Central Research Lab, India and Advanced Digital Sciences Center, Singapore. His research interests include object detection, instance segmentation, pedestrian detection, human object interaction, human pose estimation, crowd counting, image classification and weakly supervised learning.

Works
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About Hisham Cholakkal

Dr. Hisham Cholakkal is an Assistant Professor in the Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).
 
Biography

Dr. Hisham Cholakkal was a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi. He received PhD degree in Computer Vision from Nanyang Technological University, Singapore and M.Tech degree in Digital Signal Processing from Indian Institute of Technology Guwahati, India. He has six years of experience at various industrial and academic research labs. Before joining IIAI, he was a Senior Technical Lead in the Computer Vision and Deep learning Research team at Mercedes-Benz R&D, India. He has also worked as a computer vision researcher at BEL-Central Research Lab, India and Advanced Digital Sciences Center, Singapore. His research interests include object detection, instance segmentation, pedestrian detection, human object interaction, human pose estimation, crowd counting, image classification and weakly supervised learning.

Present	Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Computer Vision
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution

Computer Sciences, Artificial Intelligence and Robotics, Statistics and Probability, and Data Science

Object detection, instance segmentation, pedestrian detection, human object interaction, human pose estimation, crowd counting, image classification, and weakly supervised learning

http://orcid.org/0000-0002-8230-9065

Journal Articles (2)

SipMaskv2: Enhanced Fast Image and Video Instance Segmentation (Jiale Cao, Yanwei Pang, Rao Anwer, et al.) IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

We propose a fast single-stage method for both image and video instance segmentation, called SipMask, that preserves the instance spatial information by performing multiple sub-region mask predictions. The main module in our method is a ...

Towards partial supervision for generic object counting in natural scenes (Hisham Cholakkal, Guolei Sun, Salman Khan, et al.) IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

Generic object counting in natural scenes is a challenging computer vision problem. Existing approaches either rely on instance-level supervision or absolute count information to train a generic object counter. We introduce a partially supervised setting ...

SAT: Scale-Augmented Transformer for Person Search (Mustansar Fiaz, Hisham Cholakkal, Rao Muhammad Anwer, et al.) Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 (2023)

Person search is a challenging computer vision problem where the objective is to simultaneously detect and reidentify a target person from the gallery of whole scene images captured from multiple cameras. Here, the challenges related ...

Learning a Dynamic Cross-Modal Network for Multispectral Pedestrian Detection (Jin Xie, Rao Muhammad Anwer, Hisham Cholakkal, et al.) MM 2022 - Proceedings of the 30th ACM International Conference on Multimedia (2022)

Multispectral pedestrian detection that enables continuous (day and night) localization of pedestrians has numerous applications. Existing approaches typically aggregate multispectral features by a simple element-wise operation. However, such a local feature aggregation scheme ignores the ...

Fixing localization errors to improve image classification (Guolei Sun, Salman Khan, Wen Li, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020)

Deep neural networks are generally considered black-box models that offer less interpretability for their decision process. To address this limitation, Class Activation Map (CAM) provides an attractive solution that visualizes class-specific discriminative regions in an ...

Count- and Similarity-Aware R-CNN for pedestrian detection (Jin Xie, Hisham Cholakkal, Rao Muhammad Anwer, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020)

Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and proposal similarity information within a two-stage pedestrian detection ...

SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation. (Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal, et al.) arXiv: Computer Vision and Pattern Recognition (2020)

Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage methods. We propose a fast single-stage instance segmentation method, called SipMask, ...

On the Robustness of 3D Object Detectors (Fatima Albreiki, Sultan Abu Ghazal, Jean Lahoud, et al.) MMAsia '22: Proceedings of the 4th ACM International Conference on Multimedia in Asia (2022)

In recent years, significant progress has been achieved for 3D object detection on point clouds thanks to the advances in 3D data collection and deep learning techniques. Nevertheless, 3D scenes exhibit a lot of variations ...

DoodleFormer: Creative Sketch Drawing with Transformers (Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

Creative sketching or doodling is an expressive activity, where imaginative and previously unseen depictions of everyday visual objects are drawn. Creative sketch image generation is a challenging vision problem, where the task is to generate ...

Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer (Omkar Thawakar, Sanath Narayan, Jiale Cao, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features during the attention computations. We argue that such an attention computation ignores the multi-scale spatio-temporal feature relationships that ...

PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search (Mustansar Fiaz, Hisham Cholakkal, Sanath Narayan, et al.) arXiv (2022)

Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, previous study focuses on rich feature information learning, ...

PSTR: End-to-End One-Step Person Search With Transformers (Jiale Cao, Pang Yanwei, Rao Anwer, et al.) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2022)

We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for ...

CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection (Dhanalaxmi Gaddam, Jean Lahoud, Fahad Shahbaz Khan, et al.) arXiv (2022)

Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of the scene. In this work, we propose Contextualized Multi-Stage ...

Transformers in Remote Sensing: A Survey (Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Anwer, et al.) arXiv (2022)

Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures, originally introduced in natural language processing, have pervaded computer vision field where ...

AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility (Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, et al.) arXiv (2022)

One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as ...

3D Vision with Transformers: A Survey (Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan, et al.) arXiv (2022)

The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replacement for the widely used convolution operators, due to ...

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications (Muhammad Maaz, Abdelrahman Shaker, Hisham Cholakkal, et al.) arXiv (2022)

In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot be deployed on edge devices. It is of great interest to ...

Structured latent embeddings for recognizing unseen classes in unseen domains (Shivam Chandhok, Sanath Narayan, Hisham Cholakkal, et al.) arXiv (2021)

The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues ...

Handwriting Transformers (Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, et al.) arXiv (2021)

We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writing style patterns. The proposed HWT captures the long and ...

D2-Net: Weakly-supervised action localization via discriminative embeddings and denoised activations (Sanath Narayan, Hisham Cholakkal, Munawar Hayat, et al.) arXiv (2020)

This work proposes a weakly-supervised temporal action localization framework, called D2-Net, which strives to temporally localize actions using video-level supervision. Our main contribution is the introduction of a novel loss formulation, which jointly enhances the ...