activity
20232026
most citedModeling Dual Period-Varying Preferences for Takeaway Recommendation

9 citations · 21 across the 18 of their papers we have counts for

collaborators
Showing cs.CVShow all

13 papers · 1 filter

cs.CV2026

RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning

Yelin Wang, Zijia Song, Shuo Ye +6

Remote Sensing Image Change Captioning (RSICC) aims to describe changes between bi-temporal remote sensing images and holds significant research and application value. However, mos…

cs.CV2025

Quantized Visual Geometry Grounded Transformer

Weilun Feng, Haotong Qin, Mingqiang Wu +8

Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have made remarkable progress with the use of large-scale transformers. Their…

cs.CV2025

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation

Weilun Feng, Chuanguang Yang, Haotong Qin +10

Diffusion models have demonstrated remarkable performance on vision generation tasks. However, the high computational complexity hinders its wide application on edge devices. Quant…

cs.CV2025

SRKD: Towards Efficient 3D Point Cloud Segmentation via Structure- and Relation-aware Knowledge Distillation

Yuqi Li, Junhao Dong, Zeyu Dong +3

3D point cloud segmentation faces practical challenges due to the computational complexity and deployment limitations of large-scale transformer-based models. To address this, we p…

cs.CV2025

Multi-party Collaborative Attention Control for Image Customization

Han Yang, Chuanguang Yang, Qiuli Wang +4

The rapid advancement of diffusion models has increased the need for customized image generation. However, current customization methods face several limitations: 1) typically acce…

cs.CV2025

Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition

Chuanguang Yang, Xinqiang Yu, Han Yang +4

Multi-teacher Knowledge Distillation (KD) transfers diverse knowledge from a teacher pool to a student network. The core problem of multi-teacher KD is how to balance distillation…