Zobrazeno 1 - 10
of 153
pro vyhledávání: '"You, Suya"'
Human-Object Interaction (HOI) detection is a fundamental task in image understanding. While deep-learning-based HOI methods provide high performance in terms of mean Average Precision (mAP), they are computationally expensive and opaque in training
Externí odkaz:
http://arxiv.org/abs/2408.07018
Autor:
You, Yang, Uy, Mikaela Angelina, Han, Jiaqi, Thomas, Rahul, Zhang, Haotong, You, Suya, Guibas, Leonidas
Reverse engineering 3D computer-aided design (CAD) models from images is an important task for many downstream applications including interactive editing, manufacturing, architecture, robotics, etc. The difficulty of the task lies in vast representat
Externí odkaz:
http://arxiv.org/abs/2408.01437
We introduce GreenCOD, a green method for detecting camouflaged objects, distinct in its avoidance of backpropagation techniques. GreenCOD leverages gradient boosting and deep features extracted from pre-trained Deep Neural Networks (DNNs). Tradition
Externí odkaz:
http://arxiv.org/abs/2405.16144
Autor:
Zhou, Shijie, Fan, Zhiwen, Xu, Dejia, Chang, Haoran, Chari, Pradyumna, Bharadwaj, Tejas, You, Suya, Wang, Zhangyang, Kadambi, Achuta
The increasing demand for virtual reality applications has highlighted the significance of crafting immersive 3D assets. We present a text-to-3D 360$^{\circ}$ scene generation pipeline that facilitates the creation of comprehensive 360$^{\circ}$ scen
Externí odkaz:
http://arxiv.org/abs/2404.06903
Autor:
Zhou, Shijie, Chang, Haoran, Jiang, Sicheng, Fan, Zhiwen, Zhu, Zehao, Xu, Dejia, Chari, Pradyumna, You, Suya, Wang, Zhangyang, Kadambi, Achuta
3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times, some work has emerged that aims to extend the function
Externí odkaz:
http://arxiv.org/abs/2312.03203
Autor:
Yu, Zifan, Tavakoli, Erfan Bank, Chen, Meida, You, Suya, Rao, Raghuveer, Agarwal, Sanjeev, Ren, Fengbo
The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects an
Externí odkaz:
http://arxiv.org/abs/2311.02535
Previous research in $2D$ object detection focuses on various tasks, including detecting objects in generic and camouflaged images. These works are regarded as passive works for object detection as they take the input image as is. However, convergenc
Externí odkaz:
http://arxiv.org/abs/2310.18788
The increasing complexity of modern deep neural network models and the expanding sizes of datasets necessitate the development of optimized and scalable training methods. In this white paper, we addressed the challenge of efficiently training neural
Externí odkaz:
http://arxiv.org/abs/2310.10879
Unsupervised image-to-image (I2I) translation learns cross-domain image mapping that transfers input from the source domain to output in the target domain while preserving its semantics. One challenge is that different semantic statistics in source a
Externí odkaz:
http://arxiv.org/abs/2310.04995
Supervised trackers trained on labeled data dominate the single object tracking field for superior tracking accuracy. The labeling cost and the huge computational complexity hinder their applications on edge devices. Unsupervised learning methods hav
Externí odkaz:
http://arxiv.org/abs/2309.09078