10 citations · 22 across the 7 of their papers we have counts for
7 papers
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning
Bingchen Zhao, Haoqin Tu, Chen Wei +2
This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a do…
ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles
Haoqin Tu, Bowen Yang, Xianfeng Zhao
Automatically generating textual content with desired attributes is an ambitious task that people have pursued long. Existing works have made a series of progress in incorporating…
ReSee: Responding through Seeing Fine-grained Visual Knowledge in Open-domain Dialogue
Haoqin Tu, Yitong Li, Fei Mi +1
Incorporating visual knowledge into text-only dialogue systems has become a potential direction to imitate the way humans think, imagine, and communicate. However, existing multimo…
An Overview on Controllable Text Generation via Variational Auto-Encoders
Haoqin Tu, Yitong Li
Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language. T…
PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation
Haoqin Tu, Zhongliang Yang, Jinshuai Yang +2
Controllable text generation has taken a gigantic step forward these days. Yet existing methods are either constrained in a one-off pattern or not efficient enough for receiving mu…
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
Haoqin Tu, Zhongliang Yang, Jinshuai Yang +1
Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time. Nevertheless, ex…
Pixel-Stega: Generative Image Steganography Based on Autoregressive Models
Siyu Zhang, Zhongliang Yang, Haoqin Tu +2
In this letter, we explored generative image steganography based on autoregressive models. We proposed Pixel-Stega, which implements pixel-level information hiding with autoregress…