activity
20242026
most citedUniversal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations

5 citations · 6 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2026★ 1 cited

Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling

Feier Qin, Xiao Li, Yi Zheng +5

Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet most still remain unable to suppo…

eess.IV2025

Generative Latent Coding for Ultra-Low Bitrate Image and Video Compression

Linfeng Qi, Zhaoyang Jia, Jiahao Li +3

Most existing approaches for image and video compression perform transform coding in the pixel space to reduce redundancy. However, due to the misalignment between the pixel-space…

cs.CV2025

One-Step Diffusion-Based Image Compression with Semantic Distillation

Naifu Xue, Zhaoyang Jia, Jiahao Li +3

While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasing latency. In this work, we revisit the…

cs.SD2025★ 5 cited

Universal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations

Xue Jiang, Xiulian Peng, Yuan Zhang +1

Current large speech language models are mainly based on semantic tokens from discretization of self-supervised learned representations and acoustic tokens from a neural codec, fol…

cs.CV2024

Uncertainty-Aware Deep Video Compression with Ensembles

Wufei Ma, Jiahao Li, Bin Li +1

Deep learning-based video compression is a challenging task, and many previous state-of-the-art learning-based video codecs use optical flows to exploit the temporal correlation be…