Search Results for author: Navaneeth Bodla

Found 7 papers, 2 papers with code

Hierarchical Video Prediction Using Relational Layouts for Human-Object Interactions

no code implementations CVPR 2021 Navaneeth Bodla, Gaurav Shrivastava, Rama Chellappa, Abhinav Shrivastava

Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation.

Human-Object Interaction Detection Relational Reasoning +3

Soft Sampling for Robust Object Detection

1 code implementation18 Jun 2018 Zhe Wu, Navaneeth Bodla, Bharat Singh, Mahyar Najibi, Rama Chellappa, Larry S. Davis

Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset.

Robust Object Detection

Semi-supervised FusedGAN for Conditional Image Generation

no code implementations ECCV 2018 Navaneeth Bodla, Gang Hua, Rama Chellappa

We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation.

Conditional Image Generation Face Generation +1

Soft-NMS -- Improving Object Detection With One Line of Code

8 code implementations ICCV 2017 Navaneeth Bodla, Bharat Singh, Rama Chellappa, Larry S. Davis

To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.

Object Detection

Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

no code implementations15 Feb 2017 Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu, Jun-Cheng Chen, Carlos Castillo, Rama Chellappa

Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.

Face Recognition Face Verification

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