7 citations · 16 across the 8 of their papers we have counts for
9 papers
Revisiting Greedy Decoding for Visual Question Answering: A Calibration Perspective
Boqi Chen, Xudong Liu, Yunke Ao +1
Stochastic sampling strategies are widely adopted in large language models (LLMs) to balance output coherence and diversity. These heuristics are often inherited in Multimodal LLMs…
Mask What Matters: Mitigating Object Hallucinations in Multimodal Large Language Models with Object-Aligned Visual Contrastive Decoding
Boqi Chen, Xudong Liu, Jianing Qiu
We study object hallucination in Multimodal Large Language Models (MLLMs) and improve visual contrastive decoding (VCD) by constructing an object-aligned auxiliary view. We leverag…
AMUSE: Audio-Visual Benchmark and Alignment Framework for Agentic Multi-Speaker Understanding
Sanjoy Chowdhury, Karren D. Yang, Xudong Liu +6
Recent multimodal large language models (MLLMs) such as GPT-4o and Qwen3-Omni show strong perception but struggle in multi-speaker, dialogue-centric settings that demand agentic re…
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
Jiaming Yan, Jianchun Liu, Hongli Xu +4
Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under com…
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
Qili Zhang, Qianren Mao, Yangyifei Luo +15
Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output…
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Philip Anastassiou, Jiawei Chen, Jitong Chen +43
We introduce Seed-TTS, a family of large-scale autoregressive text-to-speech (TTS) models capable of generating speech that is virtually indistinguishable from human speech. Seed-T…
Controllable and Lossless Non-Autoregressive End-to-End Text-to-Speech
Zhengxi Liu, Qiao Tian, Chenxu Hu +5
Some recent studies have demonstrated the feasibility of single-stage neural text-to-speech, which does not need to generate mel-spectrograms but generates the raw waveforms direct…
Dual-Flattening Transformers through Decomposed Row and Column Queries for Semantic Segmentation
Ying Wang, Chiuman Ho, Wenju Xu +3
It is critical to obtain high resolution features with long range dependency for dense prediction tasks such as semantic segmentation. To generate high-resolution output of size $H…
An Empirical Study on End-to-End Singing Voice Synthesis with Encoder-Decoder Architectures
Dengfeng Ke, Yuxing Lu, Xudong Liu +3
With the rapid development of neural network architectures and speech processing models, singing voice synthesis with neural networks is becoming the cutting-edge technique of digi…