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pro vyhledávání: '"A Chandraker"'
In this work, we take a deeper look into the diverse factors that influence the efficacy of modern unsupervised domain adaptation (UDA) methods using a large-scale, controlled empirical study. To facilitate our analysis, we first develop UDA-Bench, a
Externí odkaz:
http://arxiv.org/abs/2409.15264
Autor:
Kalluri, Tarun, Lee, Jihyeon, Sohn, Kihyuk, Singla, Sahil, Chandraker, Manmohan, Xu, Joseph, Liu, Jeremiah
We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. While significant recent advances have resulted in impro
Externí odkaz:
http://arxiv.org/abs/2405.13779
Transformer-based methods have exhibited significant generalization ability when prompted with target-domain demonstrations or example solutions during inference. Although demonstrations, as a way of task specification, can capture rich information t
Externí odkaz:
http://arxiv.org/abs/2405.06063
The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions, we contend that the instantaneous detection and quantification of subtle motions is equally
Externí odkaz:
http://arxiv.org/abs/2405.02781
Photorealistic simulation plays a crucial role in applications such as autonomous driving, where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However, reconstruction qual
Externí odkaz:
http://arxiv.org/abs/2405.00900
Autor:
Yao, Manyi, Aich, Abhishek, Suh, Yumin, Roy-Chowdhury, Amit, Shelton, Christian, Chandraker, Manmohan
Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on d
Externí odkaz:
http://arxiv.org/abs/2404.15244
A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency being a high priority for scaling such models, we observed that the stat
Externí odkaz:
http://arxiv.org/abs/2404.14657
Visual program synthesis is a promising approach to exploit the reasoning abilities of large language models for compositional computer vision tasks. Previous work has used few-shot prompting with frozen LLMs to synthesize visual programs. Training a
Externí odkaz:
http://arxiv.org/abs/2404.04627
Autor:
Liang, Mingfu, Su, Jong-Chyi, Schulter, Samuel, Garg, Sparsh, Zhao, Shiyu, Wu, Ying, Chandraker, Manmohan
Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed percept
Externí odkaz:
http://arxiv.org/abs/2403.17373
We introduce LaGTran, a novel framework that utilizes text supervision to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain gaps. While unsupervised adaptation methods have been established to
Externí odkaz:
http://arxiv.org/abs/2403.05535