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pro vyhledávání: '"LI, Jinlong"'
Referring 3D Segmentation is a visual-language task that segments all points of the specified object from a 3D point cloud described by a sentence of query. Previous works perform a two-stage paradigm, first conducting language-agnostic instance segm
Externí odkaz:
http://arxiv.org/abs/2410.13294
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy. Although recent studies have highlighted the efficacy of vehicle-to-everything (V2X) communication techniques in autonomous driving, a sign
Externí odkaz:
http://arxiv.org/abs/2409.10699
Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression. However, strong external priors from Multimodal Large Language Models (MLLMs) have not been explicitly explored in video compression.
Externí odkaz:
http://arxiv.org/abs/2408.08093
Autor:
Xu, Xiaoxu, Yuan, Yitian, Li, Jinlong, Zhang, Qiudan, Jie, Zequn, Ma, Lin, Tang, Hao, Sebe, Nicu, Wang, Xu
In this paper, we propose 3DSS-VLG, a weakly supervised approach for 3D Semantic Segmentation with 2D Vision-Language Guidance, an alternative approach that a 3D model predicts dense-embedding for each point which is co-embedded with both the aligned
Externí odkaz:
http://arxiv.org/abs/2407.09826
Efficient fine-tuning of vision-language models (VLMs) like CLIP for specific downstream tasks is gaining significant attention. Previous works primarily focus on prompt learning to adapt the CLIP into a variety of downstream tasks, however, sufferin
Externí odkaz:
http://arxiv.org/abs/2407.08374
Graph contrastive learning (GCL) aims to contrast positive-negative counterparts to learn the node embeddings, whereas graph data augmentation methods are employed to generate these positive-negative samples. The variation, quantity, and quality of n
Externí odkaz:
http://arxiv.org/abs/2406.15044
Autor:
Li, Jinlong, Li, Baolu, Tu, Zhengzhong, Liu, Xinyu, Guo, Qing, Juefei-Xu, Felix, Xu, Runsheng, Yu, Hongkai
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems. However, these systems often struggle in low-light condi
Externí odkaz:
http://arxiv.org/abs/2404.04804
Autor:
Li, Baolu, Li, Jinlong, Liu, Xinyu, Xu, Runsheng, Tu, Zhengzhong, Guo, Jiacheng, Li, Xiaopeng, Yu, Hongkai
Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions
Externí odkaz:
http://arxiv.org/abs/2403.11371
Autor:
Li, Jinlong, Pan, Shucheng
We present a cross-architecture high-order heterogeneous Navier-Stokes simulation solver, XFluids, for compressible reacting multicomponent flows on different platforms. The multi-component reacting flows are ubiquitous in many scientific and enginee
Externí odkaz:
http://arxiv.org/abs/2403.05910
Publikováno v:
Chinese Physics B 32, 106103 (2023)
Hydrogen and lithium, along with their compounds, are crucial materials for nuclear fusion research. High-pressure studies have revealed intricate structural transitions in all these materials. However, research on lithium hydrides beyond LiH has mos
Externí odkaz:
http://arxiv.org/abs/2402.15791