Song Wang

20
Papers
97
Total Citations

Papers (20)

Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning

CVPR 2025
28
citations

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

ICLR 2025
17
citations

Bidirectional Autoregessive Diffusion Model for Dance Generation

CVPR 2024
15
citations

CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models

ICLR 2025arXiv
13
citations

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations

ICCV 2025
7
citations

Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective

AAAI 2025
5
citations

PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning

CVPR 2025arXiv
4
citations

Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration

CVPR 2025arXiv
3
citations

Monocular Semantic Scene Completion via Masked Recurrent Networks

ICCV 2025
3
citations

Uncertainty-Instructed Structure Injection for Generalizable HD Map Construction

CVPR 2025
1
citations

BrainMAP: Learning Multiple Activation Pathways in Brain Networks

AAAI 2025
1
citations

Tuning-Free Accountable Intervention for LLM Deployment – a Metacognitive Approach

AAAI 2025
0
citations

Orthogonal Dictionary Guided Shape Completion Network for Point Cloud

AAAI 2024
0
citations

Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation

CVPR 2024
0
citations

From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration

CVPR 2024
0
citations

The Source Image is the Best Attention for Infrared and Visible Image Fusion

ICCV 2025
0
citations

MGMap: Mask-Guided Learning for Online Vectorized HD Map Construction

CVPR 2024
0
citations

SAM4D: Segment Anything in Camera and LiDAR Streams

ICCV 2025
0
citations

DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation

CVPR 2025
0
citations

Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning

AAAI 2025
0
citations