3 citations · 3 across the 12 of their papers we have counts for
15 papers
PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts
Ruixiao Dong, Jianmin Bao, Bozhi Luan +6
Pixel-space diffusion models provide an appealing alternative to latent-space diffusion models by directly modeling raw images without relying on a learned tokenizer. However, scal…
Geometry-Aware Dataset Condensation for Diffusion Model Training
Xiao Cui, Yulei Qin, Mo Zhu +3
Dataset condensation aims to construct compact datasets from real data via synthesis or selection. However, existing approaches are ill-suited for diffusion model training: synthet…
EchoGen: Generating Visual Echoes in Any Scene via Feed-Forward Subject-Driven Auto-Regressive Model
Ruixiao Dong, Zhendong Wang, Keli Liu +5
Subject-driven generation is a critical task in creative AI; yet current state-of-the-art methods present a stark trade-off. They either rely on computationally expensive, per-subj…
Multi-Level Aware Preference Learning: Enhancing RLHF for Complex Multi-Instruction Tasks
Ruopei Sun, Jianfeng Cai, Jinhua Zhu +5
RLHF has emerged as a predominant approach for aligning artificial intelligence systems with human preferences, demonstrating exceptional and measurable efficacy in instruction fol…
Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering
Jianfeng Cai, Wengang Zhou, Zongmeng Zhang +3
Multimodal large language models (MLLMs) have achieved remarkable progress in video understanding.However, hallucination, where the model generates plausible yet incorrect outputs,…
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF
Kangwen Zhao, Jianfeng Cai, Jinhua Zhu +5
Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often suffers from reward hacking, wh…
Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression
Hanyue Tu, Siqi Wu, Li Li +2
Autoencoder-based structures have dominated recent learned image compression methods. However, the inherent information loss associated with autoencoders limits their rate-distorti…
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling
Jianfeng Cai, Jinhua Zhu, Ruopei Sun +4
Reinforcement Learning from Human Feedback (RLHF) has achieved considerable success in aligning large language models (LLMs) by modeling human preferences with a learnable reward m…
LaneTCA: Enhancing Video Lane Detection with Temporal Context Aggregation
Keyi Zhou, Li Li, Wengang Zhou +3
In video lane detection, there are rich temporal contexts among successive frames, which is under-explored in existing lane detectors. In this work, we propose LaneTCA to bridge th…
SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection
Yonghui Wang, Shaokai Liu, Li Li +2
Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, result…
Forest2Seq: Revitalizing Order Prior for Sequential Indoor Scene Synthesis
Qi Sun, Hang Zhou, Wengang Zhou +2
Synthesizing realistic 3D indoor scenes is a challenging task that traditionally relies on manual arrangement and annotation by expert designers. Recent advances in autoregressive…
Prediction and Reference Quality Adaptation for Learned Video Compression
Xihua Sheng, Li Li, Dong Liu +1
Temporal prediction is one of the most important technologies for video compression. Various prediction coding modes are designed in traditional video codecs. Traditional video cod…
Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction
Zhiyang Guo, Wengang Zhou, Li Li +2
3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant repre…
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression
Xihua Sheng, Li Li, Dong Liu +1
Video compression performance is closely related to the accuracy of inter prediction. It tends to be difficult to obtain accurate inter prediction for the local video regions with…
Progressive Recurrent Network for Shadow Removal
Yonghui Wang, Wengang Zhou, Hao Feng +2
Single-image shadow removal is a significant task that is still unresolved. Most existing deep learning-based approaches attempt to remove the shadow directly, which can not deal w…