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Mengmeng Jing

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Dropout Prompt Learning: Towards Robust and Adaptive Vision-Language Models

Biao Chen
Lin Zuo
Mengmeng Jing
Kunbin He
Yuchen Wang

Dropout is a widely used regularization technique which improves the generalization ability of a model by randomly dropping neurons. In light of this, we propose Dropout Prompt Learning, which aims for applying dropout to improve the robustness of the vision-language models. Different from the vanilla dropout, we apply dropout on the tokens of the textual and visual branches, where we evaluate the token significance considering both intra-modal context and inter-modal alignment, enabling flexible dropout probabilities for each token. Moreover, to maintain semantic alignment for general knowledge transfer while encouraging the diverse representations that dropout introduces, we further propose residual entropy regularization. Experiments on 11 benchmarks show our method's effectiveness in challenging scenarios like low-shot learning, long-tail classification, and out-of-distribution generalization. Notably, our method surpasses regularization-based methods including KgCoOp by 5.10% and PromptSRC by 2.13% in performance on base-to-novel generalization.

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AAAI Conference 2026 Conference Paper

Instruction-Guided Cross-Modal Clustering for Training-Free Visual Token Pruning in Vision-Language Models

Yunqian Yu
Biao Chen
Yunya Zhang
Tonglan Xie
Mengmeng Jing
Lin Zuo

Large vision-language models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data such as images and text. However, the number of visual tokens in these models often far exceeds that of textual tokens, resulting in substantial redundancy and high inference costs. Existing pruning methods primarily rely on either unimodal information or cross-modal attention mechanisms. The former often overlooks the semantic alignment between instructions and visual representations in the multimodal space, while the latter is prone to attention drift and dispersion, leading to significant performance degradation under high pruning ratios. All the above issues stem from the lack of effective textual guidance during the pruning process. To identify effective informational cues for guiding pruning, we conduct an in-depth analysis of the interaction between language instructions and visual features based on the cross-modal information bottleneck attribution (CIBA) method, revealing the presence of noun anchors. Based on this analysis, we propose the Instruction-Guided Cross-Modal Clustering Token Pruning (ICCTP) method, a plug-and-play, training-free pruning paradigm. Specifically, ICCTP first leverages global attention to retain a small set of visual tokens that preserve global context. It then extracts nouns from the instruction as clustering centers to perform cross-modal clustering over the remaining visual tokens. To balance semantic diversity and global relevance while reducing intra-cluster redundancy, we design an importance scoring mechanism. Finally, visual tokens within each cluster are pruned according to a specified pruning ratio. We evaluate ICCTP on multiple VLM architectures, including LLaVA-1.5-7B, LLaVA-1.5-13B, and LLaVA-NeXT-7B. Experimental results show that ICCTP maintains strong performance across various pruning rates without requiring retraining. Notably, even under an extreme setting where 94.4% of visual tokens are removed, ICCTP retains 90.02% of the original accuracy while reducing TFLOPs by 82.36%.

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ICLR Conference 2025 Conference Paper

Rethinking Spiking Neural Networks from an Ensemble Learning Perspective

Yongqi Ding
Lin Zuo
Mengmeng Jing
Pei He
Hanpu Deng

Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that share architectures and weights, and highlight a crucial issue that affects their performance: excessive differences in initial states (neuronal membrane potentials) across timesteps lead to unstable subnetwork outputs, resulting in degraded performance. To mitigate this, we promote the consistency of the initial membrane potential distribution and output through membrane potential smoothing and temporally adjacent subnetwork guidance, respectively, to improve overall stability and performance. Moreover, membrane potential smoothing facilitates forward propagation of information and backward propagation of gradients, mitigating the notorious temporal gradient vanishing problem. Our method requires only minimal modification of the spiking neurons without adapting the network structure, making our method generalizable and showing consistent performance gains in 1D speech, 2D object, and 3D point cloud recognition tasks. In particular, on the challenging CIFAR10-DVS dataset, we achieved 83.20\% accuracy with only four timesteps. This provides valuable insights into unleashing the potential of SNNs.

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NeurIPS Conference 2025 Conference Paper

Synergy Between the Strong and the Weak: Spiking Neural Networks are Inherently Self-Distillers

Yongqi Ding
Lin Zuo
Mengmeng Jing
Kunshan Yang
Pei He
Tonglan Xie

Brain-inspired spiking neural networks (SNNs) promise to be a low-power alternative to computationally intensive artificial neural networks (ANNs), although performance gaps persist. Recent studies have improved the performance of SNNs through knowledge distillation, but rely on large teacher models or introduce additional training overhead. In this paper, we show that SNNs can be naturally deconstructed into multiple submodels for efficient self-distillation. We treat each timestep instance of the SNN as a submodel and evaluate its output confidence, thus efficiently identifying the strong and the weak. Based on this strong and weak relationship, we propose two efficient self-distillation schemes: (1) Strong2Weak: During training, the stronger "teacher" guides the weaker "student", effectively improving overall performance. (2) Weak2Strong: The weak serve as the "teacher", distilling the strong in reverse with underlying dark knowledge, again yielding significant performance gains. For both distillation schemes, we offer flexible implementations such as ensemble, simultaneous, and cascade distillation. Experiments show that our method effectively improves the discriminability and overall performance of the SNN, while its adversarial robustness is also enhanced, benefiting from the stability brought by self-distillation. This ingeniously exploits the temporal properties of SNNs and provides insight into how to efficiently train high-performance SNNs.

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AAAI Conference 2024 Conference Paper

Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Networks

Yongqi Ding
Lin Zuo
Mengmeng Jing
Pei He
Yongjun Xiao

Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of low-power neuromorphic computing. However, existing SNNs suffer from significant latency, utilizing 10 to 40 timesteps or more, to recognize neuromorphic objects. At low latencies, the performance of existing SNNs is drastically degraded. In this work, we propose the Shrinking SNN (SSNN) to achieve low-latency neuromorphic object recognition without reducing performance. Concretely, we alleviate the temporal redundancy in SNNs by dividing SNNs into multiple stages with progressively shrinking timesteps, which significantly reduces the inference latency. During timestep shrinkage, the temporal transformer smoothly transforms the temporal scale and preserves the information maximally. Moreover, we add multiple early classifiers to the SNN during training to mitigate the mismatch between the surrogate gradient and the true gradient, as well as the gradient vanishing/exploding, thus eliminating the performance degradation at low latency. Extensive experiments on neuromorphic datasets, CIFAR10-DVS, N-Caltech101, and DVS-Gesture have revealed that SSNN is able to improve the baseline accuracy by 6.55% ~ 21.41%. With only 5 average timesteps and without any data augmentation, SSNN is able to achieve an accuracy of 73.63% on CIFAR10-DVS. This work presents a heterogeneous temporal scale SNN and provides valuable insights into the development of high-performance, low-latency SNNs.

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NeurIPS Conference 2022 Conference Paper

Variational Model Perturbation for Source-Free Domain Adaptation

Mengmeng Jing
Xiantong Zhen
Jingjing Li
Cees Snoek

We aim for source-free domain adaptation, where the task is to deploy a model pre-trained on source domains to target domains. The challenges stem from the distribution shift from the source to the target domain, coupled with the unavailability of any source data and labeled target data for optimization. Rather than fine-tuning the model by updating the parameters, we propose to perturb the source model to achieve adaptation to target domains. We introduce perturbations into the model parameters by variational Bayesian inference in a probabilistic framework. By doing so, we can effectively adapt the model to the target domain while largely preserving the discriminative ability. Importantly, we demonstrate the theoretical connection to learning Bayesian neural networks, which proves the generalizability of the perturbed model to target domains. To enable more efficient optimization, we further employ a parameter sharing strategy, which substantially reduces the learnable parameters compared to a fully Bayesian neural network. Our model perturbation provides a new probabilistic way for domain adaptation which enables efficient adaptation to target domains while maximally preserving knowledge in source models. Experiments on several source-free benchmarks under three different evaluation settings verify the effectiveness of the proposed variational model perturbation for source-free domain adaptation.

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AAAI Conference 2021 Conference Paper

Balanced Open Set Domain Adaptation via Centroid Alignment

Mengmeng Jing
Jingjing Li
Lei Zhu
Zhengming Ding
Ke Lu
Yang Yang

Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA methods. To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples. Specifically, to reduce the domain gaps, we first project the features to a hyperspherical latent space. In this space, we propose to bound the centroid deviation angles to not only increase the intraclass compactness but also enlarge the inter-class margins. With the bounded centroid deviation angles, we employ the statistical Extreme Value Theory to recognize the unknown samples that are misclassified into known classes. In addition, to learn better centroids, we propose an improved centroid update strategy based on sample reweighting and adaptive update rate to cooperate with centroid alignment. Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes.

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AAAI Conference 2019 Conference Paper

From Zero-Shot Learning to Cold-Start Recommendation

Jingjing Li
Mengmeng Jing
Ke Lu
Lei Zhu
Yang Yang
Zi Huang

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging problems in computer vision and recommender system, respectively. In general, they are independently investigated in different communities. This paper, however, reveals that ZSL and CSR are two extensions of the same intension. Both of them, for instance, attempt to predict unseen classes and involve two spaces, one for direct feature representation and the other for supplementary description. Yet there is no existing approach which addresses CSR from the ZSL perspective. This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR. Specifically, we propose a Lowrank Linear Auto-Encoder (LLAE), which challenges three cruxes, i. e. , domain shift, spurious correlations and computing efficiency, in this paper. LLAE consists of two parts, a low-rank encoder maps user behavior into user attributes and a symmetric decoder reconstructs user behavior from user attributes. Extensive experiments on both ZSL and CSR tasks verify that the proposed method is a win-win formulation, i. e. , not only can CSR be handled by ZSL models with a significant performance improvement compared with several conventional state-of-the-art methods, but the consideration of CSR can benefit ZSL as well.

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