6 papers
Towards Large Model Feature Coding
Youwei Pang, Changsheng Gao, Dong Liu +2
Large models have delivered remarkable performance across a wide range of perception and generation tasks, yet practical deployment is increasingly constrained by computational and…
Federated Martingale Posterior Samping
Boning Zhang, Matteo Zecchin, Mingzhao Guo +2
Federated Bayesian neural networks require fixing a prior on the model parameters together with a likelihood. Eliciting meaningful priors on the weight space of modern overparamete…
TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling
Xinmin Feng, Li Li, Dong Liu +1
To fit diverse display and bandwidth constraints, high-frame-rate videos are temporally downscaled to low-frame-rate (LFR) and later upscaled, requiring joint optimization for effe…
TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation
Yinda Chen, Haoyuan Shi, Xiaoyu Liu +5
Neuron segmentation from electron microscopy (EM) volumes is crucial for understanding brain circuits, yet the complex neuronal structures in high-resolution EM images present sign…
Dual form Complementary Masking for Domain-Adaptive Image Segmentation
Jiawen Wang, Yinda Chen, Xiaoyu Liu +4
Recent works have correlated Masked Image Modeling (MIM) with consistency regularization in Unsupervised Domain Adaptation (UDA). However, they merely treat masking as a special fo…
Conditional Latent Coding with Learnable Synthesized Reference for Deep Image Compression
Siqi Wu, Yinda Chen, Dong Liu +1
In this paper, we study how to synthesize a dynamic reference from an external dictionary to perform conditional coding of the input image in the latent domain and how to learn the…