← Search

Qingyong Li

7 accepted papers

2026

MOBO: A Merging-Oriented Bi-Level Optimization Framework for Class Incremental Learning

IJCAI 2026

Class-Incremental Learning (CIL) aims to enable models to sequentially learn new tasks while retaining knowledge from previous ones. Recently, merging-based pre-trained CIL methods have gained significant attention due to their competitive performance and high inference efficiency. However, most exi

Cited by 0Scholar
2026

PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splatting

CVPR 2026

Unsupervised point cloud segmentation is critical for embodied artificial intelligence and autonomous driving, as it mitigates the prohibitive cost of dense point-level annotations required by fully supervised methods. While integrating 2D pre-trained models such as the Segment Anything Model (SAM)

Cited by 0SourceScholar
2026

Revisiting the Role of Pretrained Weights in Model Merging: On Near-Optimality within the Core Subspace

ICML 2026poster

Model merging offers an efficient solution for integrating task-specific knowledge from multiple fine-tuned models. Most existing approaches focus on manipulating the difference vectors between fine-tuned and pre-trained weights, often overlooking the generalization capabilities inherent in the pret…

Cited by 0SourceScholar
2025

CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging

ICML 2025poster

Multi-task model merging offers a promising paradigm for integrating multiple expert models into a unified system without additional training. Existing state-of-the-art techniques, such as Task Arithmetic and its variants, merge models by accumulating task vectors—defined as the parameter difference…

Cited by 0SourcePDFScholar
2025

Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration

NeurIPS 2025poster

Multi-task model merging aims to consolidate knowledge from multiple fine-tuned task-specific experts into a unified model while minimizing performance degradation. Existing methods primarily approach this by minimizing differences between task-specific experts and the unified model, either from a p…

Cited by 0SourcecodeScholar
2023

Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning

CVPR 2023poster

The dilemma between plasticity and stability arises as a common challenge for incremental learning. In contrast, the human memory system is able to remedy this dilemma owing to its multi-level memory structure, which motivates us to propose a Bilevel Memory system with Knowledge Projection (BMKP) fo…

2021

TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition

ICCV 2021poster

A table arranging data in rows and columns is a very effective data structure, which has been widely used in business and scientific research. Considering large-scale tabular data in online and offline documents, automatic table recognition has attracted increasing attention from the document analys…

Cited by 69PDFcodeScholar