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pro vyhledávání: '"Bodla, Navaneeth"'
Autor:
Saini, Nirat, Bodla, Navaneeth, Shrivastava, Ashish, Ravichandran, Avinash, Zhang, Xiao, Shrivastava, Abhinav, Singh, Bharat
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 controlled manipulation of objects and blending them seamlessly into
Externí odkaz:
http://arxiv.org/abs/2407.10958
Autor:
Sarkar, Rohan, Bodla, Navaneeth, Vasileva, Mariya I., Lin, Yen-Liang, Beniwal, Anurag, Lu, Alan, Medioni, Gerard
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 present a framework, OutfitTransformer, that uses the proposed task-spec
Externí odkaz:
http://arxiv.org/abs/2204.04812
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and a conventional detection schema for accurate generic object detection. Motivated by the effectiveness of regionlets for model
Externí odkaz:
http://arxiv.org/abs/1811.11318
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 generate an incorrect training signal for the detector. Interestingly, we
Externí odkaz:
http://arxiv.org/abs/1806.06986
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 measures of a good image generation model. Most existing models are i
Externí odkaz:
http://arxiv.org/abs/1801.05551
In this paper, we propose a novel object detection framework named "Deep Regionlets" by establishing a bridge between deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the abilities of regionle
Externí odkaz:
http://arxiv.org/abs/1712.02408
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 score is selected and all other detection boxes with a significant ov
Externí odkaz:
http://arxiv.org/abs/1704.04503
Autor:
Bodla, Navaneeth, Zheng, Jingxiao, Xu, Hongyu, Chen, Jun-Cheng, Castillo, Carlos, Chellappa, Rama
Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks are more receptive to pose and illumination others appear to capture mor
Externí odkaz:
http://arxiv.org/abs/1702.04471
Autor:
Sarkar, Rohan, Bodla, Navaneeth, Vasileva, Mariya I., Lin, Yen-Liang, Beniwal, Anurag, Lu, Alan, Medioni, Gerard
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
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 present a framework, OutfitTransformer, that uses the proposed task-spec
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