Generative-based Fusion Mechanism for Multi-Modal Tracking

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 generative techniques to address the critical challenge, information fusion, in multi-modal tracking. In this paper, we delve into two prominent GM techniques, namely, Conditional Generative Adversarial Networks (CGANs) and Diffusion Models (DMs). Different from the standard fusion process where the features from each modality are directly fed into the fusion block, we condition these multi-modal features with random noise in the GM framework, effectively transforming the original training samples into harder instances. This design excels at extracting discriminative clues from the features, enhancing the ultimate tracking performance. To quantitatively gauge the effectiveness of our approach, we conduct extensive experiments across two multi-modal tracking tasks, three baseline methods, and three challenging benchmarks. The experimental results demonstrate that the proposed generative-based fusion mechanism achieves state-of-the-art performance, setting new records on LasHeR and RGBD1K

Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method

RGBT tracking draws increasing attention due to its robustness in multi-modality warranting (MMW) scenarios, such as nighttime and bad weather, where relying on a single sensing modality fails to ensure stable tracking results. However, the existing benchmarks predominantly consist of videos collected in common scenarios where both RGB and thermal infrared (TIR) information are of sufficient quality. This makes the data unrepresentative of severe imaging conditions, leading to tracking failures in MMW scenarios. To bridge this gap, we present a new benchmark, MV-RGBT, captured specifically in MMW scenarios. In contrast with the existing datasets, MV-RGBT comprises more object categories and scenes, providing a diverse and challenging benchmark. Furthermore, for severe imaging conditions of MMW scenarios, a new problem is posed, namely \textit{when to fuse}, to stimulate the development of fusion strategies for such data. We propose a new method based on a mixture of experts, namely MoETrack, as a baseline fusion strategy. In MoETrack, each expert generates independent tracking results along with the corresponding confidence score, which is used to control the fusion process. Extensive experimental results demonstrate the significant potential of MV-RGBT in advancing RGBT tracking and elicit the conclusion that fusion is not always beneficial, especially in MMW scenarios. Significantly, the proposed MoETrack method achieves new state-of-the-art results not only on MV-RGBT, but also on standard benchmarks, such as RGBT234, LasHeR, and the short-term split of VTUAV (VTUAV-ST). More information of MV-RGBT and the source code of MoETrack will be released at https://github.com/Zhangyong-Tang/MoETrack

The Ninth Visual Object Tracking VOT2021 Challenge Results

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Nonsingular Global Fixed-Time Stabilization for Nonlinear Systems

Since existing results about fixed-time stabilization are only applied to strict feedback systems, this paper investigates the nonsingular fixed-time stabilization of more general high-order nonlinear systems. Based on a novel concept named coordinate mapping of time domain, a control method is first proposed to transform the nonsingular fixed-time convergence problem into the finite-time convergence problem of a transformed time-varying system. By extending the existing, adding a power integrator technique into the considered time-varying system, a periodic controller is constructed to stabilize the original system in fixed time. The results of simulations verify the effectiveness of the proposed method.</jats:p

Nonsingular Global Fixed-Time Stabilization for Nonlinear Systems

Since existing results about fixed-time stabilization are only applied to strict feedback systems, this paper investigates the nonsingular fixed-time stabilization of more general high-order nonlinear systems. Based on a novel concept named coordinate mapping of time domain, a control method is first proposed to transform the nonsingular fixed-time convergence problem into the finite-time convergence problem of a transformed time-varying system. By extending the existing, adding a power integrator technique into the considered time-varying system, a periodic controller is constructed to stabilize the original system in fixed time. The results of simulations verify the effectiveness of the proposed method

Trans Influence of Boryl Ligands in CO2 Hydrogenation on Ruthenium Complexes: Theoretical Prediction of Highly Active Catalysts for CO2 Reduction

In this work, we study the trans influence of boryl ligands and other commonly used non-boryl ligands in order to search for a more active catalyst than the ruthenium dihydride complex Ru(PNP)(CO)H2 for the hydrogenation of CO2. The theoretical calculation results show that only the B ligands exhibit a stronger trans influence than the hydride ligand and are along increasing order of trans influence as follows: –H &lt; –BBr2 &lt; –BCl2 ≈ –B(OCH)2 &lt; –Bcat &lt; –B(OCH2)2 ≈ –B(OH)2 &lt; –Bpin &lt; –B(NHCH2)2 &lt; –B(OCH3)2 &lt; –B(CH3)2 &lt; –BH2. The computed activation free energy for the direct hydride addition to CO2 and the NBO analysis of the property of the Ru–H bond indicate that the activity of the hydride can be enhanced by the strong trans influence of the B ligands through the change in the Ru–H bond property. The function of the strong trans influence of B ligands is to decrease the d orbital component of Ru in the Ru–H bond. The design of a more active catalyst than the Ru(PNP)(CO)H2 complex is possible