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Lanxiao Wang

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Ego-PMOVE: Prompt-aware Mixture of View Experts Network for Egocentric Gaze Prediction

Heqian Qiu
Lanxiao Wang
Taijin Zhao
Zhaofeng Shi
Xiang Li
Linfeng Xu
Hongliang Li

Egocentric gaze prediction serves as a critical indicator for decoding human visual attention and cognitive processes, but its inherently limited field of view creates prediction challenges. Although exo-view data provides supplementary contextual information, it exhibits significant spatial and semantic gaps. Existing methods focus solely on isolated feature encoding in single-view paradigms, neglecting cross-view gaze correlations. To make up for this gap, we make the first exploration of cross-view gaze relationship for egocentric gaze prediction, and propose Ego-PMOVE, a novel Prompt-aware Mixture of View Experts network. Unlike prior cross-view studies that forcibly align cross-view features thereby introducing inference noise, we leverage the popular Mixture-of-Experts (MoE) and a set of flexible prompts to disentangle features from different views into three parallel experts: a view-shared expert directly modeling common semantic relationships, a view-discrepancy expert adaptively adjusting the spatial position, scale and shifts based on different view-specific features, and an egocentric expert extracting independent features to compensate for the case of missing exocentric data. To balance these experts, we further design a soft router to dynamically weight them for mining useful information while suppressing noise. A view-query gaze decoder then generates view-specific gaze attention maps, jointly optimized by gaze-heamap and cross-view contrastive loss that regularize both shared and divergent features for accurate gaze prediction. Extensive experiments across the multi-view EgoMe dataset and single-view Ego4D and EGTEA Gaze++ datasets demonstrate the effectiveness and generalizability of our approach.

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AAAI Conference 2026 Conference Paper

Parameter Merging with Gradient-Guided Supermasks in Online Continual Learning

Benliu Qiu
Heqian Qiu
Lanxiao Wang
Taijin Zhao
Yu Dai
Lili Pan
Hongliang Li

Online continual learning (OCL) aims at learning a non-stationary data stream in a way of reading each data sample only once, and hence suffers from the trade-off of catastrophic forgetting and insufficient learning. In this work, we firstly analytically establish relationship between loss functions and model parameters from the Bayesian perspective. Based on our analysis, we subsequently propose a parameter merging method with gradient-guided supermasks. Our method leverages 1-order and 2-order gradient information to construct supermasks that determine the merging weights between the old and new models. Our method performs direct arithmetic operations on parameters to update models, beyond traditional gradient descent. We further discover that a widely-used premise that 1-order gradients can be negligible is invalid in OCL, due to slow convergence incurred by insufficient learning. Additionally, we utilize a dual-model dual-view distillation strategy that can align output distributions of the new and merged models for each sample, further enhancing model performance. Extensive experiments are conducted on four benchmarks in OCL settings, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-100. Experimental results demonstrate that our method is effective, and achieves a substantial boost over previous methods.

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ICML Conference 2023 Conference Paper

Optimizing Mode Connectivity for Class Incremental Learning

Haitao Wen
Haoyang Cheng
Heqian Qiu
Lanxiao Wang
Lili Pan 0001
Hongliang Li 0001

Class incremental learning (CIL) is one of the most challenging scenarios in continual learning. Existing work mainly focuses on strategies like memory replay, regularization, or dynamic architecture but ignores a crucial aspect: mode connectivity. Recent studies have shown that different minima can be connected by a low-loss valley, and ensembling over the valley shows improved performance and robustness. Motivated by this, we try to investigate the connectivity in CIL and find that the high-loss ridge exists along the linear connection between two adjacent continual minima. To dodge the ridge, we propose parameter-saving OPtimizing Connectivity (OPC) based on Fourier series and gradient projection for finding the low-loss path between minima. The optimized path provides infinite low-loss solutions. We further propose EOPC to ensemble points within a local bent cylinder to improve performance on learned tasks. Our scheme can serve as a plug-in unit, extensive experiments on CIFAR-100, ImageNet-100, and ImageNet-1K show consistent improvements when adapting EOPC to existing representative CIL methods. Our code is available at https: //github. com/HaitaoWen/EOPC.

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