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Interns Group, Interns Group > Interim Engineering Intern - HW
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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: _
Minimum Qualification:
Pytorch
Neural network architecture development and evaluation
Computer Vision
Educational Requirements:
Computer Science, Computer Engineering
: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail myhr.support@qualcomm.com or call Qualcomm's toll-free number found here (https://qualcomm.service-now.com/hrpublic?id=hr_public_article_view&sysparm_article=KB0039028) . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
If you would like more information about this role, please contact Qualcomm Careers (http://www.qualcomm.com/contact/corporate) .
EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification
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