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Yawen Cui

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3 papers

2 author rows

AAAI Conference 2025 Conference Paper

Few-Shot Audio-Visual Class-Incremental Learning with Temporal Prompting and Regularization

Yawen Cui
Li Liu
Zitong Yu
Guanjie Huang
Xiaopeng Hong

Audio-Visual Learning (AVL) aims at the audio-visual perception with both audio and vision modalities. AVL also suffers from data insufficiency in many applications as with other unimodal tasks. Concurrently, AVL often needs to continuously learn over time rather than all knowledge simultaneously. Considering the above two perspectives, our work mainly focuses on benchmarking the unexplored Few-Shot Audio-Visual Class-Incremental Learning (FS-AVCIL), i.e., continually perceiving novel categories described by a limited number of labeled examples with audio and visual modalities. Firstly, we provide the detailed task configuration together with a thorough analysis of the challenges in FS-AVCIL: (1) how to efficiently learn and fuse multimodal information with limited labeled examples; and (2) how to alleviate catastrophic forgetting cross-modal semantic correlations with limited data. Then, we propose an efficient framework based on Vision Transformer to solve FS-AVCIL. This framework contains two parts: temporal-residual prompting for audio-visual synergy adapter and temporal prompt regularization. Specifically, temporal-residual prompting is incorporated into the audio-visual adapter to efficiently finetune the pre-trained foundation model with limited data and capture audio-visual correlation by learning temporal-relevant prompts. Besides, we regularize temporal-relevant prompts to memorize previous knowledge by fully using the temporal knowledge from various perspectives. This framework is validated in audio-visual classification tasks under the FS-AVCIL scenario, and extensive experiments demonstrate its superior performance.

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ECAI Conference 2025 Conference Paper

MAGiC: An LLM-Powered Multi-Agent Framework for Unleashing Visual Creativity

Shilong Wang 0002
Jian Zhao 0006
Yawen Cui
Chi Zhang 0012
Xuelong Li 0001

Humans can complete high-quality creative work, such as drawing a picture or creating a video based on text, and editing images or videos according to textual requirements. In the earlier period of artificial intelligence, the “best of N” strategy was often utilized to leverage the creative capability of multiple visual creators, which was computationally inefficient and labor-intensive. With the emergence of large language models (LLMs), the LLM-based agent dynamically plans the invocation of tools to accomplish creative tasks. However, these agent systems struggle to achieve optimal tool planning and creative performance, especially complex creative tasks. Toward these issues, we propose MAGiC, a LLM-Powered Multi-Agent Framework for Visual Generation and Editing to unleash Visual Creativity. MAGiC addresses users’ creation requirements through the collaboration of four modules, i. e. , task assignment, planning, execution, and evaluation, with each controlled by agents configured for different roles. Specifically, the task assignment module iteratively releases new tasks based on the user requirements and its completion progress. The Planning module configures the corresponding Planner for different types of tasks, and these Planners create detailed plans for the tasks they are responsible for. The Execution module iteratively executes the plans set by the Planners. The evaluation module assesses the result obtained by the execution module to prevent errors from affecting subsequent tasks. Finally, MAGiC illustrates excellent creativity on multiple tasks, especially complex ones, and the high scalability of MAGiC is an initial step in applying multi-agent systems to llm-based visual system.

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ICML Conference 2025 Conference Paper

Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning

Run He
Di Fang 0004
Yicheng Xu
Yawen Cui
Ming Li 0011
Cen Chen 0002
Ziqian Zeng
Huiping Zhuang

Exemplar-Free Class-Incremental Learning (EFCIL) aims to sequentially learn from distinct categories without retaining exemplars but easily suffers from catastrophic forgetting of learned knowledge. While existing EFCIL methods leverage knowledge distillation to alleviate forgetting, they still face two critical challenges: semantic shift and decision bias. Specifically, the embeddings of old tasks shift in the embedding space after learning new tasks, and the classifier becomes biased towards new tasks due to training solely with new data, hindering the balance between old and new knowledge. To address these issues, we propose the Dual-Projection Shift Estimation and Classifier Reconstruction (DPCR) approach for EFCIL. DPCR effectively estimates semantic shift through a dual-projection, which combines a learnable transformation with a row-space projection to capture both task-wise and category-wise shifts. Furthermore, to mitigate decision bias, DPCR employs ridge regression to reformulate a classifier reconstruction process. This reconstruction exploits previous in covariance and prototype of each class after calibration with estimated shift, thereby reducing decision bias. Extensive experiments demonstrate that, on various datasets, DPCR effectively balances old and new tasks, outperforming state-of-the-art EFCIL methods. Our codes are available at https: //github. com/RHe502/ICML25-DPCR.

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