52 citations · 71 across the 5 of their papers we have counts for
7 papers
DTG-Restore: Training-Free Diffusion Refinement for Generative Video Super-Resolution
Hidir Yesiltepe, Koutilya PNVR, Gaurav Pathak +4
Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the strong coupling between con…
InVi: Object Insertion In Videos Using Off-the-Shelf Diffusion Models
Nirat Saini, Navaneeth Bodla, Ashish Shrivastava +4
We introduce InVi, an approach for inserting or replacing objects within videos (referred to as inpainting) using off-the-shelf, text-to-image latent diffusion models. InVi targets…
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation
Rohan Sarkar, Navaneeth Bodla, Mariya I. Vasileva +4
Learning an effective outfit-level representation is critical for predicting the compatibility of items in an outfit, and retrieving complementary items for a partial outfit. We pr…
Soft Sampling for Robust Object Detection
Zhe Wu, Navaneeth Bodla, Bharat Singh +3
We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will gen…
Semi-supervised FusedGAN for Conditional Image Generation
Navaneeth Bodla, Gang Hua, Rama Chellappa
We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality…
Soft-NMS -- Improving Object Detection With One Line of Code
Navaneeth Bodla, Bharat Singh, Rama Chellappa +1
Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum…