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Le professeur James Clark fait partie du département de génie électrique et informatique de l'Université McGill et est directeur du laboratoire de recherche sur la motricité visuelle au Centre de Recherche sur les Machines Intelligentes et codirecteur du McGill Retail Innovation Lab (MRIL).
2023
Q. Tian, T. Arbel, and J.J. Clark, “Grow-push-prune: Aligning deep discriminants for effective structural network compression”. Computer Vision and Image Understanding, 231, p.103682, 2023.
H.-Y. Chang, S. Hasan Mozafari, C. Chen, J.J. Clark, B. Meyer, and W. Gross, "PipeBERT: High-throughput BERT inference for ARM big.LITTLE Multi-Core Processors”, Journal of Signal Processing Systems, 95 (7), 877-894.
A. Edalati, M. Tahaei, I. Kobyzev, V. Partovi Nia, J.J. Clark, and M. Rezagholizadeh, "KronA: Parameter Efficient Tuning with Kronecker Adapter", 3rd Neurips Workshop on Efficient Natural Language and Speech Processing. December 2023.
R. Sherkati, and J.J. Clark, "Clustered Saliency Prediction", in 2023 British Machine Vision Conference, November 2023.
S.-H. Mozafari, J.J. Clark, W. Gross and B. Meyer, "Training Acceleration of Frequency Domain CNNs Using Activation Compression", 2023 IEEE International Symposium on Circuits and Systems, May 2023.
S.-H. Mozafari, J.J. Clark, W. Gross and B. Meyer, "Efficient 1D Grouped Convolution for PyTorch A Case Study: Fast On-Device Fine-Tuning For SqueezeBERT", 34th IEEE International Conference on Application-specific Systems, Architectures and Processors, 2023.
L. Shen, I. Amara, R. Li, B. Meyer, W. Gross, and J.J. Clark, "Fast Fine-Tuning using Curriculum Domain Adaptation", CRV 2023, Montreal.
A. Mosleh, M.S. Tahaei, J.J. Clark, and V. Partovi-Nia, "Towards Low-cost Learning-based Camera ISP via Unrolled Optimization", CRV 2023, Montreal.
F. Askari, R. Jiang, Z. Li, J. Niu, Y. Shi, and J.J. Clark, "Self-Supervised Video Interaction Classification using Image Representation of Skeleton Data", CVSports Workshop at CVPR 2023.
H.Y. Chang, S.-H. Mozafari, J.J. Clark, B. Meyer and W. Gross, "High-Throughput Edge Inference for BERT models via Neural Architecture Search and Pipeline', GLSVLSI 2023.
Y. Wang, A. Chubarau, H.J. Yoo, T. Akhavan, and J.J. Clark, "Age-specific perceptual image quality assessment," Image Quality and System Performance Conference, at IS&T Electronic Imaging 2023.
S.B. Rangrej, K. Liang, T. Hassner, and J.J. Clark, "GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction", Winter Conference on Applications of Computer Vision (WACV), Waikaloa Hawaii, January 2023.
2022
Charles Le et al. “Efficient Two-Stage Progressive Quantization of BERT”. In: Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP). 2022, pp. 1–9.
Zahra Vaseqi et al. “A Framework for Video- Text Retrieval with Noisy Supervision”. In: Proceedings of the 2022 International Conference on Multimodal Interaction. 2022, pp. 373–383.
M Abdelgawad et al. “BERTPerf: Inference Latency Predictor for BERT on ARM big. LITTLE Multi-Core Processors”. In: 2022 IEEE Workshop on Signal Processing Systems (SiPS). IEEE. 2022, pp. 1–6.
Samrudhdhi B Rangrej, Chetan L Srinidhi, and James J Clark. “Consistency driven sequential transformers attention model for partially observable scenes”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pp. 2518– 2527.
Danilo Vucetic et al. “Efficient Fine-Tuning of Compressed Language Models with Learners”. In: 2022.
ML Kornelsen et al. “Fast Heterogeneous Task Mapping for Reducing Edge DNN Latency”. In: 2022 IEEE 33rd International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE. 2022, pp. 64– 71.
Farzaneh Askari et al. “Interaction Classification with Key Actor Detection in Multi- Person Sports Videos”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pp. 3580–3588.
Lulan Shen et al. “Conjugate Adder Net (CAddNet)-a Space-Efficient Approximate CNN”. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022, pp. 2793–2797.
Ibtihel Amara et al. “CES-KD: Curriculumbased Expert Selection for Guided Knowledge Distillation”. In: 2022 26th International Conference on Pattern Recognition (ICPR). IEEE. 2022, pp. 1901–1907.
Negin Firouzian et al. “Work-in-Progress: Utilizing latency and accuracy predictors for efficient hardware-aware NAS”. In: 2022 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS). IEEE. 2022, pp. 15–16.
Ali Edalati et al. “Kronecker decomposition for gpt compression”. In: (2022).
Hang Zhang et al. “Towards Finding Efficient Students Via Blockwise Neural Architecture Search and Knowledge Distillation”. In: Edge Intelligence Workshop, Montreal. Ď㽶ĘÓƵ (Canada), 2022.
Amir Ardakani et al. “Standard Deviation- Based Quantization for Deep Neural Networks”. In: 2022.
Ali Edalati et al. “Kronecker decomposition for gpt compression”. In: Edge Intelligence Workshop, Montreal. 2022.
Ziaeefard M. Meyer B. Gross W. Amara I. and J.J. Clark. “On the Importance of Integrating Curriculum Design for Teacher Assistant-based Knowledge Distillation”. In: Edge Intelligence Workshop, Montreal. 2022.
Mozafari S. Clark J.J. Gross W. Chang H-Y. and B. Meyer. “NAS plus Pipeline for High Throughput Edge Inference BERT”. In: Edge Intelligence Workshop, Montreal. 2022.
Mozafari S. Clark J.J. Meyer B. Kornelson M. and W. Gross. “ARMCL BERT: Novel Quantizable BERT Implementation for ARM SoCs”. In: Edge Intelligence Workshop, Montreal. 2022.
Mozafari S. Clark J.J. Gross W. Li C. and B. Meyer. “BERT Inference Energy Predictor for Efficient Hardware-aware NAS”. In: Edge Intelligence Workshop, Montreal. 2022.
Meyer B. Gross W. Shen L. and J.J. Clark. “Retention of Domain Adaptability in Compressed Neural Networks”. In: Edge Intelligence Workshop, Montreal. 2022.
Ardakani A. Clark J.J. Meyer B. Le C. and W. Gross. “Dyadic Integer Only BERT”. In: Edge Intelligence Workshop, Montreal. 2022.
Mozafari S. Clark J.J. Gross W.J. Firouzian N. and Meyer B.H. “Latency and Accuracy Predictors for Efficient BERT Hardware-aware NAS”. In: Edge Intelligence Workshop, Montreal. 2022.
2021
S. Hasan Mozafari, J. Clark, W. Gross, and B. Meyer. “Implementing Convolutional Neural Networks Using Hartley Stochastic Computing with Adaptive Rate Feature Map Compression,” IEEE Open Journal of Circuits and Systems 2, 805-819
A.A. Boatswain Jacques, V.I. Adamchuk, J. Park, G. Cloutier, J.J. Clark, and C. Miller. “Towards a Machine Vision-Based Yield Monitor for the Counting and Quality Mapping of Shallots,” Frontiers in Robotics and AI, 8:627067
Q. Tian, T. Arbel, and J.J. Clark. “Task dependent deep LDA pruning of neural network,” Computer Vision and Image Understanding, 203, p.103154, January 2021
S. Rangrej and J. J. Clark. “A Probabilistic Hard Attention Model For Sequentially Observed Scenes,” 2021 British Machine Vision Conference
S. Hasan Mozafari, J.J. Clark, W. Gross, and B. Meyer. “Hartley Stochastic Computing For Convolutional Neural Networks,” 2021 International Workshop on Signal Processing Systems, 1-6
A. Edalati, M. Tahaei, A. Rashid, V. Partovi Nia, J.J. Clark, and M. Rezagholizadeh. “Kronecker Decomposition for GPT Compression,” NeurIPS 2021 Efficient Natural Language and Speech Processing Workshop.
S. Vadacchino, J.J. Clark, and T. Arbel. “HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images,” MIDL 2021 conference.
K. Sayar, N. Muthukrishnan, P. Savadjiev, S. Bhatnagar, J.J. Clark, and R. Forghani. “Medical Image Analysis Using Standard Radiomic Features and Mean Curvature of Isophotes for Prediction of Cervical Lymph Node Metastasis,” 2021 SIIM Conference on Machine Intelligence in Medical Imaging.
J.J. Clark. “Active Sensor (Eye) Movement Control,” in Computer Vision: A Reference Guide (2021 edition), ed K. Ikeuchi, Springer US, New York, 2021.
A. Edalati, M. Tahaei, A. Rashid, V.P. Nia, J.J. Clark, and M. Rezagholizadeh. “Kronecker Decomposition for GPT Compression,” arXiv preprint arXiv:2110.08152. 2021 Oct 15.
A. Chubarau and J.J. Clark. “VTAMIQ: Transformers for Attention Modulated Image Quality Assessment,” arXiv preprint arXiv:2110.01655. 2021 Oct 4. S.B. Rangrej and J.J. Clark. “Visual Attention in Imaginative Agents,” arXiv preprint arXiv:2104.00177. 2021 Apr 1.
S. Vadacchino, R.Mehta, N.M. Sepahvand, B. Nichyporuk, J.J. Clark, and T. Arbel. “HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images,” arXiv e-prints. 2021 Mar:arXiv-2103.
2020
Chubarau, A., Akhavan, T., Yoo, H., Mantiuk, R. and Clark, J.J., “Perceptual Image Quality Assessment for Various Viewing Conditions and Display Systems", Electronic Imaging: Image Quality and System Performance XVII, Burlingame, USA, January 2020.
Tian, Q., Arbel, T. and Clark, J.J., “Deep LDA-Pruned Nets and their Robustness ", Edge Intelligence Workshop, Montreal, March 2020.
Amara, I. and Clark, J.J., “Uncertainty Transfer with Knowledge Distillation", Edge Intelligence Workshop, Montreal, March 2020.
2019
Ra, K. and Clark, J.J., “Decoupled Hybrid 360DEG Panoramic Stereo Video”. International Conference on 3D Vision (3DV), Quebec City, September 2019.
Corcoran, G. and Clark, J.J., “Traffic Risk Assessment: A Two-Stream Approach Using Dynamic-Attention”, Computer and Robot Vision Conference (CRV), Kingston, Canada, May 2019.
Hu, G. and Clark, J.J., “Instance Segmentation based Semantic Matting for Compositing Applications”, Computer and Robot Vision Conference (CRV), Kingston, Canada, May 2019.
2018
Tian, Q., Arbel, T., Clark, J.J., “Structured Deep Fisher Pruning for Efficient Facial Trait Classification’’, Image and Vision Computing, Special issue on Biometrics in the Wild, Volume 77, pp. 45-59, September 2018.
Boatswain Jacques, A.A., Adamchuk, V., Cloutier, G., Clark, J.J., Miller, C., “Development of a Machine Vision Yield Monitor for Shallot Onion Harvesters”, 14th International Conference on Precision Agriculture, Montreal, Canada, June 2018.
Gorji, S. and Clark, J.J., "Going from Image to Video Saliency: Augmenting Image Salience with Dynamic Attentional Push", IEEE Confer-ence on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, June 2018.
Yu, B. and Clark, J.J., "WAYLA - Generating Images from Eye Movements", Computer and Robot Vision Conference (CRV), Toronto, Cana-da, May 2018.
Ward, G., Rafi Nazari, M., Soudi, A., Akhavan, T., Yoo, H., Gerhardt, J., Clark, J.J., "Mitigating Color Deficiency in Graphical Display”, Society for Information Display’s Display Week Symposium, Los Angeles USA, May 2018.
2017
Bouchard, J. and Clark, J.J., “Quality Control of Stereoscopic 3-D Compositing Using Half-Occlusion Geometry”, SMPTE Motion Imaging Journal, No. 9, pp 48-58, 2017.
Bouchard, J. and Clark, J.J., “Half-Occluded Regions: The Key to Detecting a Diverse Array of Defects in S3D Imagery”, IC3D 2017 (Stereopsia), Brussels, Belgium, December 2017.
Tian, Q., Arbel, T. and Clark, J.J., “Deep LDA-Pruned Nets for Efficient Facial Gender Classification”, IEEE Computer Society Workshop on Biometrics 2017, Hawaii, July 2017.
Gorji, S. and Clark, J.J., “Attentional Push: A Deep Convolutional Network for Augmenting Image Salience with Shared Attention Modeling in Social Scenes”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, July 2017.
