11 accepted papers
Large language models (LLMs) face significant deployment challenges due to their massive computational demands. While pruning offers a promising compression solution, existing methods suffer from two critical limitations: (1) They neglect activation distribution shifts between calibration data and t
LiDAR-inertial odometry(LIO) has been widely applied in intelligent robotics and autonomous driving, providing high-precision and low-latency ego-motion estimation. However, the massive point clouds generated by LiDAR introduce intensive data processing demands, making k-nearest neighbor(KNN) search
LiDAR-inertial odometry(LIO) has been widely applied in intelligent robotics and autonomous driving, providing high-precision and low-latency ego-motion estimation. However, the massive point clouds generated by LiDAR introduce intensive data processing demands, making k-nearest neighbor(KNN) search…
Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones, where the unlabeled set consists of both base and novel classes. Since clustering methods are time-consuming at inference, parametric-based approaches have become more popular…
Community engagement plays a critical role in anti-poaching efforts, yet existing mathematical models aimed at enhancing this engagement often overlook direct participation by community members as alternative patrollers. Unlike professional rangers, community members typically lack flexibility and e…
With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to r…
Infrared and visible image fusion (IVIF) aims to generate fused images with prominent targets and rich scene information. However, in low-light conditions, visible images lose accurate texture and color, reducing their ability to provide detailed scene information for fusion. Existing IVIF methods o…
Acquiring pairwise noisy-clean training data is challenging. Consequently, some self-supervised denoising methods utilize noisy image pairs as both input and target for network training. However, a major issue with these methods is the gap between the clean images of the input and target. In this pa…
Hierarchical Text Classification (HTC) is an essential yet challenging task in natural language processing (NLP) due to its complex label structure. Recently, a number of approaches have employed prompt learning in HTC, achieving noteworthy outcomes. However, prompt-based HTC does not further optimi…
Hierarchical text classification (HTC) is a significant but challenging task in natural language processing (NLP) due to its complex taxonomic label hierarchy. Recently, there have been a number of approaches that applied prompt learning to HTC problems, demonstrating impressive efficacy. The majori…
This paper presents a novel zero-shot method for jointly denoising and enhancing real-word low-light images. The proposed method is independent of training data and noise distribution. Guided by illumination we integrate denoising and enhancing processes seamlessly enabling end-to-end training. Pair…