23 papers · ranked by Valyu relevance
Tesfaldet, Mattie, Harley, Adam W. + 6 more
Tracking a point through a video can be a challenging task due to uncertainty arising from visual obfuscations, such as appearance changes and occlusions. Although current state-of-the-art discriminative models excel in regressing long-term point trajectory estimates—even through occlusions—they are limited to…
Madhu Kiran, Le Thanh Nguyen-Meidine, Rajat Sahay, Rafael M. O. Cruz + 2 more
'Louis-Antoine Blais-Morin' 'Éric Granger'] Siamese trackers perform similarity matching with templates (i.e., target models) to recursively localize objects within a search region. Several strategies have been proposed in the literature to update a template based on the tracker output, typically extracted from the…
Swaraj Kaondal, Arsalan Taassob, Sara Jeon, Su Hyun Lee + 6 more
Object tracking in microscopy videos is crucial for understanding biological processes. While existing methods often require fine-tuning tracking algorithms to fit the image dataset, here we explored an alternative paradigm: augmenting the image time-lapse dataset to fit the tracking algorithm. To test this approach…
Yifan Bai, Zeyang Zhao, Yihong Gong, Xing Wei
We present ARTrackV2, which integrates two pivotal aspects of tracking: determining where to look (localization) and how to describe (appearance analysis) the target object across video frames. Building on the foundation of its predecessor, ARTrackV2 extends the concept by introducing a unified generative framework to…
Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht
Digital time-lapse microscopy allows for large-scale observations of cells over time, providing a deeper understanding of cellular processes [3, 6, 19]. However, to fully harness the potential of time-lapse imaging, automated cell tracking approaches are needed, which can provide a quantitative analysis of cell…
Zhangyong Tang, Tianyang Xu, Xuefeng Zhu, Xiaojun Wu + 1 more
—Generative models (GMs) have received increasing research interest for their remarkable capacity to achieve comprehensive understanding. However, their potential application in the domain of multi-modal tracking has remained relatively unexplored. In this context, we seek to uncover the potential of harnessing…
Abolfazl Zargari, Najmeh Mashhadi, S. Ali Shariati
Cells are among the most dynamic entities, constantly undergoing various processes such as growth, division, movement, and interaction with other cells as well as the environment. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed temporal and spatial information that allows…
Xiaomiao Tao, Kaijun Wu, Yongshun Wang, Panfeng Li + 2 more
'Chenshuai Bai'] Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search…
G. Ruffini, F. Castaldo, E. Lopez-Sola, R. Sanchez-Todo + 1 more
In the Kolmogorov Theory of Consciousness, agents utilize inferred compressive models to track coarse-grained data produced by simplified world models, capturing regularities that structure subjective experience and guide action planning. Here, we study the dynamical aspects of this framework by examining how the…
Kasjan Śmigielski, Natalia Piórkowska
Automated behavioral tracking is increasingly used in biological and biomedical research; however, robustness across heterogeneous imaging conditions remains a major challenge. Domain shifts caused by changes in illumination, contrast, or acquisition setup can substantially degrade the performance of computer vision…
Wen-Hsuan Chu, Lei Ke, Jianmeng Liu, Mingxiao Huo + 2 more
We tackle the challenge of generating dynamic 4D scenes from monocular, multi-object videos with heavy occlusions, and introduce GenMOJO, a novel approach that integrates rendering-based deformable 3D Gaussian optimization with generative priors for view synthesis. While existing models perform well on novel view…
Amaury Auguste, Wissam Kaddah, Marwa Elbouz, Ghislain Oudinet + 2 more
'Ayman Alfalou' 'Stefano Mariani'] In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and…
Kuan Yin, Jiangfan Feng, Shaokang Dong, Clive J. C. Phillips
Simple Summary Animal movement trajectories are effective indicators of key information such as social behavior, food acquisition, reproduction, migration, and survival strategies in animal behavior analysis. However, manual observation is still relied upon in many analysis scenarios, which is inefficient and…
Shiqi Chen, Yuhang Li, Yuntian Wang, Hanlong Chen + 1 more
Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others1-11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge12-14. Here we present optical…
Jie Zhao, Ying Gao, Chunjuan Bo, Dong Wang + 2 more
'Antonio Fernández-Caballero' 'Byung-Gyu Kim'] Visual object tracking is one of the core techniques in human-centered artificial intelligence, which is very useful for human-machine interaction. State-of-the-art tracking methods have shown their robustness and accuracy on many challenges. However, a large amount of…
Behzad Mirzaei, Hossein Nezamabadi-pour, Amir Raoof, Reza Derakhshani + 1 more
'Reza Derakhshani' 'Yun Zhang'] Object detection and tracking are vital in computer vision and visual surveillance, allowing for the detection, recognition, and subsequent tracking of objects within images or video sequences. These tasks underpin surveillance systems, facilitating automatic video annotation…
Wanxin Wu, Yuxuan Ding, Kehua Miao, Jiachen Yang
Siamese tracking is widely used in object tracking due to its efficient dual-branch symmetric structure, deep feature matching mechanism, and flexible template strategy. Existing mainstream Siamese tracking algorithms typically employ static template matching or linear combination-based template updating to localize…
Authors not listed
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery…
Matteo Dunnhofer, Antonino Furnari, Giovanni Maria Farinella, Christian Micheloni
'Christian Micheloni'] The understanding of human-object interactions is fundamental in First Person Vision (FPV). Visual tracking algorithms which follow the objects manipulated by the camera wearer can provide useful information to effectively model such interactions. In the last years, the computer vision community…
Giovanni Bolcato, Jonas Boström
Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the…
Victor H. R. Nogueira, Rishabh Sharma, Rafael V. C. Guido, Michael J. Keiser
As efforts to improve the robustness of molecular representations advance, so does the need for methods to test and validate them. We use a Variational Auto-Encoder (VAE), an unsupervised deep learning model, to generate anomalous samples of a well-known molecular string format called SELF-referencIng Embedded Strings…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Jie Lin, Mingyuan Xu, Hongming Chen
Shape-based virtual screening is a widely utilized method in ligand-based de novo drug design, aiming to identify molecules in chemical libraries that share similar 3D shapes but simultaneously possess novel 2D chemical structures compared to the reference compound. As an emerging technology, generative model is an…