11 accepted papers
Most existing RGB-Event trackers rely on strictly aligned datasets, overlooking the asynchronous spatio-temporal resolutions common in real-world scenarios. This methodological limitation impedes effective RGB-Event feature alignment and ultimately degrades tracking performance. To overcome this li
Anchor-based multi-view clustering has garnered wide attention for its ability to reduce the computational complexity of large-scale spectral clustering.However, existing methods mostly adopt a unidirectional optimization paradigm confined to sample-anchor bipartite graphs, treating the construction…
Unsupervised Domain Adaptation (UDA) is a challenging task in person search. It adapts a well-trained model from a labeled source domain to an unlabeled target domain for privacy and efficiency. Currently, most of the state-of-the-art UDA person search methods adopt multi-scale feature alignment tec
Domain-adaptive person search (DAPS) aims to transfer pedestrian detection and re-identification capabilities from a labeled source domain to an unlabeled target domain, yet faces critical challenges from domain shift: semantic confusion among overlapping instances, over-reliance on shallow features
Unsupervised Domain Adaptation (UDA) person search aims to transfer a model trained on a labeled source domain to an unlabeled target domain without using target annotations. However, existing UDA methods frequently neglect the issue of scale inconsistency between the source and target domains. Thes…
Event cameras have attracted considerable attention for object tracking due to their microsecond-level temporal resolution and wide dynamic range, yet effectively harnessing spiking neural networks (SNNs) in this domain remains challenging. In this paper, we introduce SpikeTrack, a purely spike-driv
Combining the advantages of conventional and event cameras for robust visual tracing has drawn extensive interest. However, existing tracking approaches heavily engage in complex cross-modal fusion modules, leading to higher computational complexity and training challenges. Besides, these methods ge…
Unsupervised Domain Adaptive (UDA) person search focuses on employing the model trained on a labeled source domain dataset to a target domain dataset without any additional annotations. Most effective UDA person search methods typically utilize the ground truth of the source domain and pseudo-labels…
Most existing RGB-based trackers target low frame rate benchmarks of around 30 frames per second. This setting restricts the tracker's functionality in the real world, especially for fast motion. Event-based cameras as bioinspired sensors provide considerable potential for high frame rate tracking d…
Event-based cameras bring a unique capability to tracking, being able to function in challenging real-world conditions as a direct result of their high temporal resolution and high dynamic range. These imagers capture events asynchronously that encode rich temporal and spatial information. However,…
Inspired by the complementarity between conventional frame-based and bio-inspired event-based cameras, we propose a multi-modal based approach to fuse visual cues from the frame- and event-domain to enhance the single object tracking performance, especially in degraded conditions (e.g., scenes with…