Zobrazeno 1 - 10
of 6 806
pro vyhledávání: '"Gedeon, A."'
Autor:
Tu, Weijie, Deng, Weijian, Campbell, Dylan, Yao, Yu, Zheng, Jiyang, Gedeon, Tom, Liu, Tongliang
As large multimodal models (LMMs) are increasingly deployed across diverse applications, the need for adaptable, real-world model ranking has become paramount. Traditional evaluation methods are largely dataset-centric, relying on fixed, labeled data
Externí odkaz:
http://arxiv.org/abs/2412.06461
Video action recognition has made significant strides, but challenges remain in effectively using both spatial and temporal information. While existing methods often focus on either spatial features (e.g., object appearance) or temporal dynamics (e.g
Externí odkaz:
http://arxiv.org/abs/2411.15284
We present a detailed study of a scalar differential equation with threshold state-dependent delayed feedback. This equation arises as a simplification of a gene regulatory model. There are two monotone nonlinearities in the model: one describes the
Externí odkaz:
http://arxiv.org/abs/2410.13092
Autor:
Zhang, Qixuan, Wang, Zhifeng, Zhang, Dylan, Niu, Wenjia, Caldwell, Sabrina, Gedeon, Tom, Liu, Yang, Qin, Zhenyue
Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing. Nonetheless, the potential of using visual prompts for emotion recognition in these models remains largely unexplored and untap
Externí odkaz:
http://arxiv.org/abs/2410.02244
Contrastive Language-Image Pre-training (CLIP) models have shown significant potential, particularly in zero-shot classification across diverse distribution shifts. Building on existing evaluations of overall classification robustness, this work aims
Externí odkaz:
http://arxiv.org/abs/2410.01534
In computer vision tasks, features often come from diverse representations, domains, and modalities, such as text, images, and videos. Effectively fusing these features is essential for robust performance, especially with the availability of powerful
Externí odkaz:
http://arxiv.org/abs/2410.01506
Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball
Externí odkaz:
http://arxiv.org/abs/2409.14543
Anxiety is a common mental health condition characterised by excessive worry, fear and apprehension about everyday situations. Even with significant progress over the past few years, predicting anxiety from electroencephalographic (EEG) signals, spec
Externí odkaz:
http://arxiv.org/abs/2410.00028
With the rapid advancements in multimodal generative technology, Affective Computing research has provoked discussion about the potential consequences of AI systems equipped with emotional intelligence. Affective Computing involves the design, evalua
Externí odkaz:
http://arxiv.org/abs/2409.07256
Autor:
Madan, Surbhi, Ghosh, Shreya, Sookha, Lownish Rai, Ganaie, M. A., Subramanian, Ramanathan, Dhall, Abhinav, Gedeon, Tom
Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To thi
Externí odkaz:
http://arxiv.org/abs/2409.06224