10 accepted papers
Selective deletion of data from deep models, known as unlearning, has become crucial for enforcing the right to be forgotten, while also mitigating the negative impact of flawed training data. Retraining deep models is often impractical due to data access restrictions and computational overhead. Exi
Despite the impressive capabilities of large language models (LLMs) in aspect-based sentiment analysis (ABSA), the role of syntactic information remains underexplored in LLMs. Syntactic structures are known to be crucial for capturing aspect-opinion relationships. To explore whether LLMs can effecti…
Deep learning-based malicious traffic detection requires large-scale, accurately labeled datasets. However, realistic malicious traffic datasets contain noisy labels due to annotation errors, which can degrade the performance of detection models. Existing methods for handling noisy labels involve ro…
Spatiotemporal social behavior analysis is a technique that studies the social behavior patterns of objects and estimates their risks based on their trajectories. In social public scenarios such as train stations, hidden following behavior has become one of the most challenging issues due to its pro…
Can multimodal encoder evolve when facing increasingly tough circumstances? Our work investigates this possibility in the context of continuous vision-language navigation (continuous VLN), which aims to navigate robots under linguistic supervision and visual feedback. We propose a multimodal evoluti…
Set representation has become ubiquitous in deep learning for modeling the inductive bias of neural networks that are insensitive to the input order. DeepSets is the most widely used neural network architecture for set representation. It involves embedding each set element into a latent space with d…
This paper proposes a novel real-time semantic segmentation network via frequency domain learning, called FDLNet, which revisits the segmentation task from two critical perspectives: spatial structure description and multilevel feature fusion. We first devise an image-size convolution (IS-Conv) as a…
Real-time semantic segmentation is a crucial but challenging dense prediction task for scene parsing. However, the existing CNN-based methods commonly bias the model in favor of speed-boosting compromising spatial resolution due to business requirements and hardware constrains, which impedes the hig…
The coordination of multiple driving wheels is an important issue in wheeled mobile robot design, for both maximizing tractive capability and optimizing energy consumption. This study converts a driving wheel into a pseudo-driven wheel (PDW), which is controlled to follow the motion of the robot bod
Multi-view representation learning attempts to learn a representation from multiple views and most existing methods are unsupervised. However, representation learned only from unlabeled data may not be discriminative enough for further applications (e.g., clustering and classification). For this rea…