6 accepted papers
High dynamic range (HDR) imaging is crucial for capturing real-world lighting conditions. HDR imaging is traditionally achieved either by fusing multiple exposure frames or via inverse tone mapping from a single SDR image. However, the multi-exposure HDR method is prone to motion-induced artefacts a
Abstract— Precise situational awareness is vital for the safe deployment of artificial intelligence in real-world scenarios, especially in assisted and automated driving (AAD) systems. Panoptic segmentation, which unifies semantic and instance segmentation, plays a key role in identifying objects, h…
"The pseudo-labelling algorithm is highly effective across various tasks, particularly in semi-supervised learning, yet its vulnerabilities are not always apparent on benchmark datasets, leading to suboptimal real-world performance. In this paper, we identified some channel activations in pseudo-lab…
RGB-Event single object tracking (SOT) aims to leverage the merits of RGB and event data to achieve higher performance. However, existing frameworks focus on exploring complementary appearance information within multi-modal data, and struggle to address the association problem of targets and distrac…
While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in safety-critical applications, such as automated driving systems (ADS). Counterfactual (CF) explanations have recently gain
"Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them fo…