Company:Qualcomm Canada ULC

Job Area:Interns Group, Interns Group > Interim Engineering Intern - HW

Qualcomm Overview:

Qualcomm is a company of inventors that unlocked 5G ushering in an age of rapid acceleration in connectivity and new possibilities that will transform industries, create jobs, and enrich lives. But this is just the beginning. It takes inventive minds with diverse skills, backgrounds, and cultures to transform 5Gs potential into world-changing technologies and products. This is the Invention Age - and this is where you come in.

General Summary:

 

Deep Learning Research Internship (4 months Summer 2024)  

 

Before there were smartphones or smart cities, before autonomous cars or 360° virtual reality videos, there was our technology. Headquartered in San Diego, for over 30 years Qualcomm inventions have inspired others to make the impossible, possible. From 5G to artificial intelligence, from IoT to automotive and extended reality applications, Qualcomm is inventing the technologies of an intelligently connected future, spearheading research efforts for the next global wireless standard, and collaborating with industry leaders in the wireless value chain to make this future a commercial reality.  

 

In the rapidly evolving field of 2D generative modeling, significant advancements are continually reshaping our capabilities in image synthesis and manipulation. This internship focuses on leveraging these advancements, particularly in 2D diffusion models [1, 2], to explore efficient generative approaches, such as few-step distillation [3, 4], and extend research into adjacent tasks like image editing, super-resolution, restoration, and outcropping [5, 6, 7, 8]. Another potential direction is the exploration of text-to-3D asset generation [9, 10]. 

 

Responsibilities: 

Research and develop innovative approaches in 2D diffusion models for generative modeling. 

Extend research to adjacent tasks, such as super-resolution and image restoration, with a potential exploration into text-to-3D asset generation. 

Explore and implement more efficient techniques to enhance the performance of generative models, focusing on computational efficiency. 

Conduct implementation of baselines for comparative evaluation on benchmarks, along with thorough ablation studies. 

The research conducted in this internship is aimed at advancing the field of 2D generative modeling, with the expectation of contributing to paper submissions at top-tier conferences in the field. 

 

[1] High-Resolution Image Synthesis with Latent Diffusion Models, https://arxiv.org/abs/2112.10752  
[2] SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers, https://arxiv.org/abs/2410.10629  
[3] Consistency Models, https://arxiv.org/abs/2303.01469  
[4] Adversarial Diffusion Distillation, https://arxiv.org/abs/2311.17042  

[5] Palette: Image-to-Image Diffusion Models, https://arxiv.org/abs/2111.05826  

[6] SPIRE: Semantic Prompt-Driven Image Restoration: https://arxiv.org/abs/2312.11595  

[7] Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild, https://arxiv.org/pdf/2401.13627  
[8] InstructPix2Pix: Learning to Follow Image Editing Instructions, https://arxiv.org/abs/2211.09800  
[9] DreamFusion: Text-to-3D using 2D Diffusion, https://arxiv.org/abs/2209.14988 
[10] Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model, https://arxiv.org/abs/2311.06214  

 

Programming Languages:   

Python 

 

Minimum Qualification:  

Pytorch 

Neural network architecture development and evaluation 

Computer Vision 

 

Educational Requirements:  

In study towards a Masters or PhD in the following:  

Computer Science, Computer Engineering 

Looking 1 Summer Co-op (May - August) 

 

 

 

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