116 citations · 496 across the 48 of their papers we have counts for
16 papers · 1 filter
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
ProGraph: Temporally-alignable Probability Guided Graph Topological Modeling for 3D Human Reconstruction
Hongsheng Wang, Zehui Feng, Tong Xiao +4
Current 3D human motion reconstruction methods from monocular videos rely on features within the current reconstruction window, leading to distortion and deformations in the human…
Unconstrained Model Merging for Enhanced LLM Reasoning
Yiming Zhang, Baoyi He, Shengyu Zhang +12
Recent advancements in building domain-specific large language models (LLMs) have shown remarkable success, especially in tasks requiring reasoning abilities like logical inference…
AutoGeo: Automating Geometric Image Dataset Creation for Enhanced Geometry Understanding
Zihan Huang, Tao Wu, Wang Lin +3
With the rapid advancement of large language models, there has been a growing interest in their capabilities in mathematical reasoning. However, existing research has primarily foc…
Semantic Codebook Learning for Dynamic Recommendation Models
Zheqi Lv, Shaoxuan He, Tianyu Zhan +5
Dynamic sequential recommendation (DSR) can generate model parameters based on user behavior to improve the personalization of sequential recommendation under various user preferen…
MOSS: Motion-based 3D Clothed Human Synthesis from Monocular Video
Hongsheng Wang, Xiang Cai, Xi Sun +5
Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable cha…
Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery
Hongsheng Wang, Weiyue Zhang, Sihao Liu +6
Although 3D Gaussian Splatting (3DGS) has recently made progress in 3D human reconstruction, it primarily relies on 2D pixel-level supervision, overlooking the geometric complexity…
DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation
Kairui Fu, Shengyu Zhang, Zheqi Lv +2
Due to the continuously improving capabilities of mobile edges, recommender systems start to deploy models on edges to alleviate network congestion caused by frequent mobile reques…
Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Shengyu Zhang, Ziqi Jiang, Jiangchao Yao +7
Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. W…
NieR: Normal-Based Lighting Scene Rendering
Hongsheng Wang, Yang Wang, Yalan Liu +4
In real-world road scenes, diverse material properties lead to complex light reflection phenomena, making accurate color reproduction crucial for enhancing the realism and safety o…
RemoCap: Disentangled Representation Learning for Motion Capture
Hongsheng Wang, Lizao Zhang, Zhangnan Zhong +6
Reconstructing 3D human bodies from realistic motion sequences remains a challenge due to pervasive and complex occlusions. Current methods struggle to capture the dynamics of occl…
NOVA-3D: Non-overlapped Views for 3D Anime Character Reconstruction
Hongsheng Wang, Nanjie Yao, Xinrui Zhou +4
In the animation industry, 3D modelers typically rely on front and back non-overlapped concept designs to guide the 3D modeling of anime characters. However, there is currently a l…
GaussianTalker: Speaker-specific Talking Head Synthesis via 3D Gaussian Splatting
Hongyun Yu, Zhan Qu, Qihang Yu +8
Recent works on audio-driven talking head synthesis using Neural Radiance Fields (NeRF) have achieved impressive results. However, due to inadequate pose and expression control cau…
MergeNet: Knowledge Migration across Heterogeneous Models, Tasks, and Modalities
Kunxi Li, Tianyu Zhan, Kairui Fu +6
In this study, we focus on heterogeneous knowledge transfer across entirely different model architectures, tasks, and modalities. Existing knowledge transfer methods (e.g., backbon…
AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation
Zihao Tang, Zheqi Lv, Shengyu Zhang +4
Due to privacy or patent concerns, a growing number of large models are released without granting access to their training data, making transferring their knowledge inefficient and…
ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation
Zihao Tang, Zheqi Lv, Shengyu Zhang +2
The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General In…