Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Gholami, Mohsen"'
Despite recent advances in human pose estimation (HPE), poor generalization to out-of-distribution (OOD) data remains a difficult problem. While previous works have proposed Test-Time Adaptation (TTA) to bridge the train-test domain gap by refining n
Externí odkaz:
http://arxiv.org/abs/2407.14605
Knowledge distillation from LLMs is essential for the efficient deployment of language models. Prior works have proposed data generation using LLMs for preparing distilled models. We argue that generating data with LLMs is prone to sampling mainly fr
Externí odkaz:
http://arxiv.org/abs/2403.19754
This paper proposes an end-to-end framework for generating 3D human pose datasets using Neural Radiance Fields (NeRF). Public datasets generally have limited diversity in terms of human poses and camera viewpoints, largely due to the resource-intensi
Externí odkaz:
http://arxiv.org/abs/2312.14915
This paper addresses the problem of ranking pre-trained models for object detection and image classification. Selecting the best pre-trained model by fine-tuning is an expensive and time-consuming task. Previous works have proposed transferability es
Externí odkaz:
http://arxiv.org/abs/2308.02027
Publikováno v:
In Chemical Engineering Journal 15 October 2024 498
Publikováno v:
In Separation and Purification Technology 6 September 2024 343
Autor:
Verougstraete, Brieuc, Gholami, Mohsen, Gomez-Rueda, Yamid, Pérez-Botella, Eduardo, Schoukens, Matthias, Van Assche, Tom R.C., Denayer, Joeri F.M.
Publikováno v:
In Separation and Purification Technology 19 January 2025 353 Part C
Publikováno v:
In Microporous and Mesoporous Materials 15 January 2025 382
Publikováno v:
In Neurocomputing 1 January 2025 611
This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by
Externí odkaz:
http://arxiv.org/abs/2112.11593