2 citations · 4 across the 7 of their papers we have counts for
7 papers
Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
Ming Li, Yong-Jin Liu, Fang Liu +6
Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction…
A Diffusion Model Translator for Efficient Image-to-Image Translation
Mengfei Xia, Yu Zhou, Ran Yi +2
Applying diffusion models to image-to-image translation (I2I) has recently received increasing attention due to its practical applications. Previous attempts inject information fro…
PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction
Sheng Ye, Yuze He, Matthieu Lin +8
Neural implicit representations have revolutionized dense multi-view surface reconstruction, yet their performance significantly diminishes with sparse input views. A few pioneerin…
SMaRt: Improving GANs with Score Matching Regularity
Mengfei Xia, Yujun Shen, Ceyuan Yang +3
Generative adversarial networks (GANs) usually struggle in learning from highly diverse data, whose underlying manifold is complex. In this work, we revisit the mathematical founda…
Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner
Mengfei Xia, Yujun Shen, Changsong Lei +5
A diffusion model, which is formulated to produce an image using thousands of denoising steps, usually suffers from a slow inference speed. Existing acceleration algorithms simplif…
MMPI: a Flexible Radiance Field Representation by Multiple Multi-plane Images Blending
Yuze He, Peng Wang, Yubin Hu +4
This paper presents a flexible representation of neural radiance fields based on multi-plane images (MPI), for high-quality view synthesis of complex scenes. MPI with Normalized De…
Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement
Sheng Ye, Yubin Hu, Matthieu Lin +4
The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. R…