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Tianqi Zhao

7 accepted papers

2026

DREAM: Document Recognition with Explicit Adaptive Memory

CVPR 2026

Large multimodal models (LMMs) have shown promising performance for various document recognition tasks. However, LMMs adopt implicit modeling, and the parameters lack interpretability. Inspired by recent advances in human memory and learning research, we propose an explicit multiscale prototype memo

Cited by 0SourcecodeScholar
2026

SeGO: Sensitivity-Aware Golden Optimization for Large-Scale VLM Quantization

IJCAI 2026

The deployment of Vision-Language Models (VLMs) faces memory and computational bottlenecks because of the massive parameters and intensive computations. While Post-Training Quantization (PTQ) can reduce these costs, existing methods often overlook the heterogeneity of multimodal input when applied t

Cited by 0Scholar
2026

SegMem-RAG: Adaptive Memory for Retrieval-Augmented Generation in Open-Ended Knowledge Environments

AAAI 2026technical

Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language models by grounding responses in external content. However, most RAG systems assume access to static and well-organized corpora with fixed retrieval logic. In practice, real-world sources are heterogeneous and unlab

Cited by 0SourcePDFScholar
2026

UNOP: Physics-Constrained Unsupervised Neural Operator for Long-Horizon PDE Learning on Generalized Geometries

IJCAI 2026

Unsupervised learning of neural operators is constrained by numerical instability, causing predictions to diverge in long-horizon rollouts. To address this, we present a physics-constrained unsupervised neural operator for long-horizon PDE learning on generalized geometries (UNOP). This framework re

Cited by 0Scholar
2025

Disentangled Representation Learning for Chinese Handwriting Recognition

ICASSP 2025accepted

Deep learning-based sequence modeling methods have improved the performance in Chinese handwriting recognition tasks. However, the implicit representations learned in current deep neural network models usually lack explainability and generalization ability for practical handwriting samples with dive…

Cited by 0SourceScholar