13 accepted papers
Recent advances in Transformer-based Neural Operators have enabled significant progress in data-driven solvers for Partial Differential Equations (PDEs). Most current research has focused on reducing the quadratic complexity of attention to address the resulting low training and inference efficienc
B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue’s surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Tr…
As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research focus. Dividing the DRL training process into sub-tasks and using parallel computation can effectively reduce training…
Monocular endoscopic scene reconstruction is challenging due to limited viewpoints and interference from surgical instruments. While 3D Gaussian-based methods are popular for their strong reconstruction capabilities and efficiency, they often rely on sensors or stereo depth, resulting in blurred tis…
Prototype network-based methods have made substantial progress in few-shot relation extraction (FSRE) by enhancing relation prototypes with relation descriptions. However, the distribution of relations and instances in distinct representation spaces isolates the constraints of relations on instances…
Deep learning-based bioacoustic audio analysis holds immense potential across various applications. However, existing studies in bioacoustics often focus on a limited number of species, potentially hindering the transferability of models across different species. Furthermore, the manual annotation o…
Many existing face forgery detection methods primarily revolve around learning general representations on predefined datasets and subsequently crossing these static representations to other datasets. However, these approaches could lead to catastrophic forgetting in real-world scenarios, especially…
Image deblurring is an ill-posed task, where exists infinite feasible solutions for blurry images. Modem deep learning approaches usually discard the learning of blur kernels and directly employ end-to-end supervised learning. However, supervised learning can’t handle ill-posed tasks appropriately.…
Large-scale pre-trained transformers have recently achieved remarkable success in several computer vision tasks. However, it remains highly challenging to fully fine-tune models for downstream tasks, due to the expensive computational and storage cost. Recently, Parameter-Efficient Tuning (PETuning)…
The Centralized Training with Decentralized Execution paradigm (CTDE), which trains policies centrally with additional information, is important for Multi-Agent Reinforcement Learning (MARL). For CTDE, value function factorization methods make use of state during training and factorize the value fun…
A lot of efforts have been devoted to solving the problem about complex relationship and localized cooperation among a large number of agents in large-scale multi-agent systems. However, global cooperation among all agents is also important while interactions between agents often happen locally. It…
The communication among agents is important for Multi-Agent Reinforcement Learning (MARL). In this work, we propose GraphComm, a method makes use of the relation-ships among agents for MARL communication. GraphComm takes the explicit relations (e.g., agent types), which can be provided through some…
Effective spatiotemporal feature representation is crucial to the video-based action recognition task. Focusing on discriminate spatiotemporal feature learning, we propose Attentional Fused Temporal Transformation Network (AttnTTN) for action recognition on top of popular Temporal Segment Network (T…