activity
20142024
most citedContrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

31 citations · 145 across the 32 of their papers we have counts for

collaborators

32 papers

cs.CV2024

OneChart: Purify the Chart Structural Extraction via One Auxiliary Token

Jinyue Chen, Lingyu Kong, Haoran Wei +6

Chart parsing poses a significant challenge due to the diversity of styles, values, texts, and so forth. Even advanced large vision-language models (LVLMs) with billions of paramet…

cs.CV20241 cited

Small Language Model Meets with Reinforced Vision Vocabulary

Haoran Wei, Lingyu Kong, Jinyue Chen +6

Playing Large Vision Language Models (LVLMs) in 2023 is trendy among the AI community. However, the relatively large number of parameters (more than 7B) of popular LVLMs makes it d…

cs.CV2024

Stream Query Denoising for Vectorized HD Map Construction

Shuo Wang, Fan Jia, Yingfei Liu +6

To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particul…

cs.CV2024

Slot-guided Volumetric Object Radiance Fields

Di Qi, Tong Yang, Xiangyu Zhang

We present a novel framework for 3D object-centric representation learning. Our approach effectively decomposes complex scenes into individual objects from a single image in an uns…

cs.SE20231 cited

Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation

Guang Yang, Yu Zhou, Xiangyu Zhang +3

Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particula…

cs.LG2023

Hierarchical Semi-Implicit Variational Inference with Application to Diffusion Model Acceleration

Longlin Yu, Tianyu Xie, Yu Zhu +3

Semi-implicit variational inference (SIVI) has been introduced to expand the analytical variational families by defining expressive semi-implicit distributions in a hierarchical ma…