Hi! I am Kamalesh, a final year undergrad at IIT Madras. I enjoy working on questions related to Machine Learning and specifically in Reinforcement Learning. I am currently working on problems in adversarial and robust RL along with Prof Muni Pydi in the Machine Intelligence and Learning Systems group at Paris Dauphine University. I was fortunate to spend three wondeful months in Paris supported by the Charpak Scholarship. I am also interested in Computer Vision and have been associated with the Advanced geometric computing lab under Prof Ramanthan at IIT Madras.
I am actively looking for research internships for the summer of 2024. If you’d like to talk to me about anything, get in touch!
Retrieval systems are commonly used to find relevant data in large datasets. In engineering, these systems are useful for locating specific engineering shapes in a large dataset of engineering components. When end users want to search for a shape, they prefer a two-dimensional (2D) sketch over a three-dimensional (3D) object. However, users lacking domain knowledge may struggle to generate a complete query sketch and provide a partially completed sketch instead. Retrieving relevant information from partially drawn sketches is difficult because they may have missing edges, partially drawn circles, holes, ovals, etc. Most retrieval systems compare the similarity between the query and items in the database, so incomplete sketches may be ineffective in finding the relevant information. To address this problem with incomplete sketches, we propose a new generative adversarial network called SketchCADGAN. This network uses a two-stage cascaded architecture, with the first network attempting to predict a CAD model image from an incomplete sketch and the second network using the CAD model image to predict a completed sketch. Both networks are trained together adversarially. Our approach is proven more effective than other advanced techniques through qualitative and quantitative comparisons. Furthermore, we present the results of the retrieval system using both partially drawn and completed sketches, and demonstrate that incorporating completed sketches from the suggested cascaded GAN architecture results in improved retrieval performance.
@article{KENDRE202355,title={SketchCADGAN: A generative approach for completing partially drawn query sketches of engineering shapes to enhance retrieval system performance},journal={Computers & Graphics},volume={115},pages={55-68},year={2023},issn={0097-8493},doi={https://doi.org/10.1016/j.cag.2023.06.028},author={Kendre, Prasad Pralhad and Kosalaraman, Kamalesh Kumar and Jayasree, Sanjay Santhosh Kumar and Rajan, Sreehari and Jayan, Akash and Muthuganapathy, Ramanathan},keywords={Search and retrieval, Generative adversarial networks(GANs), Sketch completion, Sketch inpainting, CAD model sketches, Incomplete sketches},}
