Muhammad Kamran Janjua
Art is Long; Time is Fleeting!
I am a Machine Learning Researcher @ Huawei, Canada working in Dr. Mohammad Salameh's team at Edmonton Research Center.
Previously, I was a graduate student with Dr. Martha White at University of Alberta, where I worked on representation learning for reinforcement learning.
Particularly I worked at the combination of offline and online RL where the goal was to learn effective representations in the offline phase to warm-start
the agent in the online setting and make sure that the agent converges quickly.
Before that, I worked as a Research Associate at Qatar Computing Research Institute (QCRI) working on interpretability in neural language models. Specifically, I worked on understanding how neural language models build (if at all) grammatical structure of language internally.
I was supervised by Dr. Hassan Sajjad.
Before that I did my undergraduate from National University of Sciences & Technology (NUST), Islamabad working in TUKL-NUST R&D Lab working with Dr. Faisal Shafait.
Over the years in this pursuit of science, I have had the pleasure of visiting AaltoVision and AaltoML lab at Aalto University, Finland to work on estimating depth from stereo setups.
I have also worked as a research intern at ARTE Lab, University of Insubria back in 2018 where I worked on designing multi-modal neural networks. My research was supervised by Dr. Ignazio Gallo.
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Research
Although I have worked mainly with computer vision, I am now interested in reinforcement learning with a focus on representation learning techniques. I work on developing methods to effectively learn representations that can be distilled in upstream tasks.
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CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Amirhosein Ghasemabadi,
Mohammad Salameh,
Muhammad Kamran Janjua,
Chunhua Zhou,
Fengyu Sun,
Di Niu
arXiv preprint arXiv:2401.15235, 2024
We present CascadedGaze Network (CGNet), an encoder-decoder architecture that employs Global Context Extractor (GCE), a novel and efficient way to learn global information for image restoration.
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GVFs in the Real World: Making Predictions Online for Water Treatment
Muhammad Kamran Janjua,
Haseeb Shah,
Martha White,
Erfan Miahi,
Marlos C Machado,
Adam White
Machine Learning, 2023
We show the importance of learning in deployment, by comparing a TD agent trained purely offline with no online updating to a TD agent that learns online. This final result is one of the first to motivate the importance of adapting predictions in real-time, for non-stationary high-volume systems in the real world.
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Movement-induced Priors for Deep Stereo
Yuxin Hou,
Muhammad Kamran Janjua,
Juho Kannala,
Arno Solin
25th International Conference on Pattern Recognition (ICPR), 2020
We propose a method for fusing stereo disparity estimation with movement-induced prior information.
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Do Cross Modal Systems Leverage Semantic Relationships?
Shah Nawaz,
Muhammad Kamran Janjua*,
Ignazio Gallo,
Arif Mahmood,
Alessandro Calefati,
Faisal Shafait
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019
We propose a new measure SemanticMap to evaluate the performance of cross modal systems. Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space.
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Deep latent space learning for cross-modal mapping of audio and visual signals
Shah Nawaz*,
Muhammad Kamran Janjua*,
Ignazio Gallo,
Arif Mahmood,
Alessandro Calefati
Digital Image Computing: Techniques and Applications (DICTA), 2019
We propose a novel deep training algorithm for joint representation of audio and visual information which consists of a single stream network (SSNet) coupled with a novel loss function to learn a shared deep latent space representation of multimodal information.
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Git Loss for Deep Face Recognition
Alessandro Calefati*,
Muhammad Kamran Janjua*,
Shah Nawaz,
Ignazio Gallo
British Machine Vision Conference (BMVC), 2018
code
In order to further enhance the discriminative capability of deep features, we introduce a joint supervision signal, Git loss, which leverages on softmax and center loss functions. The aim of our loss function is to minimize the intra-class variations as well as maximize the inter-class distances.
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