8 accepted papers
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the computational perspective, however, DKL becomes challenging when the input dimension of the GP layer is high. To address t…
Deep reinforcement learning (DRL) methods have shown significant promise in controlling the movement of quadruped robots. However, for systems like hexapod robots, which feature a higher-dimensional action space, it remains challenging for an agent to devise an effective control strategy directly. C
Depression is a severe mental illness, and extracting emotional information from video-audio signals for multimodal depression recognition is a challenging problem. Recent methods use the self-attention (SA) mechanism from Transformers to capture the dynamic relationships between different modalitie…
Reinforcement learning (RL) has demonstrated impressive performance in legged locomotion over various challenging environments. However, due to the sim-to-real gap and lack of explainability, unconstrained RL policies deployed in the real world still suffer from inevitable safety issues, such as joi
Specialized robots, such as legged robots and unmanned aerial vehicles (UAVs), are commonly regarded as effective platforms for aiding in search and rescue (SAR) missions. However, existing approaches often decouple the tasks between UAVs and legged robots, for instance, using UAVs for mapping and l
Recently, image coding for machines (ICM) has been playing an important role in facilitating intelligent vision tasks. Unfortunately, the existing ICM methods separately compress features at each scale, neglecting the redundancy across multi-scale features. To address this issue, this paper proposes…
Detecting 3D lanes from the camera is a rising problem for autonomous vehicles. In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view. With this transformation, we can get rid of the perspective effects so th…
In this paper, we propose a novel deep learning based video saliency prediction method, named DeepVS. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which includes 32 subjects' fixations on 538 videos. We find from LEDOV that human attention is more likely to be at…