Aug 2026· ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)· 0 citations· 37 references
TL;DR
The proposed CARL framework employs two parallel ARL branches and aggregates their Mahalanobis distances during inference to improve prediction performance and is compared with recent methods using three widely recognized datasets.
Abstract
Exemplar-free class-incremental learning (EFCIL) poses the challenge that models cannot access data from previous tasks when learning new classes, leading to catastrophic forgetting. Recent methods freeze the feature extractor after the initial task and adapt only the classification mechanism to new classes, achieving strong performance. However, they largely overlook improving the frozen feature extractor's adaptability to unseen classes, which limits further performance gains. To address this limitation, we propose a novel complementary asymmetric representation learning (CARL) framework to enhance the model's adaptability to unseen classes. The core of CARL is the asymmetric representation learning (ARL) architecture, which combines a base encoder that extracts discriminative features for the initial task with a projection multilayer perceptron (MLP) head appended to its output. By introducing the projection head, the original output representation of the base encoder becomes an intermediate representation in the projected branch, encouraging the encoder to learn more generalizable features. This allows the model to preserve discriminative representations for the initially learned classes while improving its adaptability to unseen classes. In addition, the CARL framework employs two parallel ARL branches and aggregates their Mahalanobis distances during inference to improve prediction performance. To evaluate the efficacy of CARL, we compare it with recent methods using three widely recognized datasets. The proposed approach improves average accuracy over the best competing method by 3.70 percentage points on CIFAR-100, 2.63 percentage points on Tiny-ImageNet, and 2.90 percentage points on ImageNet-Subset.
Miles decouples the learnable modules with the pre-trained model, exploiting prior information from intermediate features of the backbone network to enable more flexible parameter expansion, and orchestrating an efficient expansion of the parameter space through guided optimization.
Kai Jiang, Zisong Lin, Hongyuan Zhang et al.· IEEE Transactions on Image P...· 0 citations
Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.
Dezheng Han, Anlan Zhang, Zhiwu Zhu et al.· 0 citations
FACET proposes an efficient replay-free task-conditioned feature consistency loss, aiming to mitigate catastrophic forgetting of the learned mixture distribution in the adapter's feature space, and demonstrates robust scalability.
This paper aims to develop a method that learns independent models for each session that can inherently prevent catastrophic forgetting and demonstrates the state-of-the-art performance on CIFAR-100 and mini-ImageNet datasets.
Yi Yin, Wanxia Deng, Jing Zhang et al.· Entropy· 0 citations
Fully few-shot class-incremental audio classification (FFCAC) requires recognizing new sound classes from only a handful of labeled examples per session, without forgetting previously learned classes and without any large base dataset. Existing methods typically freeze a pre-trained audio--language encoder and classify with point prototypes, but they suffer from significant performance degradation throughout the sessions due to generic feature representations. We propose SPECTRA, a framework built on a frozen encoder which adds three components. (i) a lightweight trainable adapter that calibrates the generic embeddings to the task; (ii) subspace feature replay, an exemplar-free anti-forgetting scheme that replays old classes by sampling from the low-rank subspace of their stored features; and (iii) a transductive optimal-transport refinement of prototypes at test time. Our central finding is that the subspace structure of the replay diminishes forgetting and outperforms naive Gaussian replay of equal variance. On three FFCAC benchmarks (NSynth-100, FSC-89, LS-100), SPECTRA improves average accuracy and reduces forgetting over current state-of-the-art methods, and our ablations statistically validate each component.
Giries Abu Ayoub, Loay Mualem, Simon Korman· 0 citations
Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes with only a few samples while avoiding forgetting base classes. However, current methods show a tendency to misclassify novel-class samples into base classes, which we find to be caused by the excessive focus on base-class-discriminative regions on novel-class samples. In this work, we aim to explore the underlying mechanism for an interpretation and solution. We first provide a compositional view to analyze the transferred and reused spatial patterns on novel-class samples. Then, through extensive experiments and theoretical analysis, we identify both empirically and theoretically that a shortcut exists in the model's base-class training, which naturally forms the excessive focus on only the most discriminative regions (primitives), which we term as the regional shortcut. Finally, based on this interpretation, to address this problem, we propose a compositional-learning-based method to learn two primitive sets (a common set and a discriminative set), which alleviates the regional shortcut by constraining the model to learn and utilize the common primitive set for base- and novel-class recognition. Extensive experiments on standard FSCIL benchmarks demonstrate the effectiveness of our approach, yielding consistent improvements over existing state-of-the-art methods in both accuracy and interpretability.
Haichen Zhou, Y. Lyu, Yixiong Zou et al.· IEEE transactions on multime...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
