Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning

• URL: http://arxiv.org/abs/2603.21708v1
• Date: Mon, 23 Mar 2026 08:50:00 GMT
• Title: Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning
• Abstract summary: Long-tail class incremental learning (LT CIL) remains highly challenging because the scarcity of samples in tail classes hampers their learning.<n>We exploit the informativeness and scalability of language knowledge to tackle these issues.<n>We introduce stratified adaptive language guidance, which leverages learnable weights to merge multi-scale semantic representations.
• Score: 50.795452298625996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-tail class incremental learning (LT CIL) remains highly challenging because the scarcity of samples in tail classes not only hampers their learning but also exacerbates catastrophic forgetting under continuously evolving and imbalanced data distributions. To tackle these issues, we exploit the informativeness and scalability of language knowledge. Specifically, we analyze the LT CIL data distribution to guide large language models (LLMs) in generating a stratified language tree that hierarchically organizes semantic information from coarse to fine grained granularity. Building upon this structure, we introduce stratified adaptive language guidance, which leverages learnable weights to merge multi-scale semantic representations, thereby enabling dynamic supervisory adjustment for tail classes and alleviating the impact of data imbalance. Furthermore, we introduce stratified alignment language guidance, which exploits the structural stability of the language tree to constrain optimization and reinforce semantic visual alignment, thereby alleviating catastrophic forgetting. Extensive experiments on multiple benchmarks demonstrate that our method achieves state of the art performance.

Related papers

• Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation? [49.7935995447581]
  We investigate whether low-resource machine translation can benefit from structured intermediate steps of linguistic analysis and grammatical reasoning.<n>We propose a pipeline for automatically generating step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks.<n>Our results show that linguistic reasoning traces are most effective as inference-time guidance.
  arXiv  Detail & Related papers  (2026-06-02T15:36:12Z)
• LinguDistill: Recovering Linguistic Ability in Vision- Language Models via Selective Cross-Modal Distillation [19.168565866461584]
  Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representation shift and cross-modal interference.<n>We propose LinguDistill, an adapter-free distillation method that restores linguistic capability by utilizing the original frozen LM as a teacher.
  arXiv  Detail & Related papers  (2026-04-01T12:38:27Z)
• SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion [11.686307370683922]
  Link prediction in knowledge graphs requires integrating structural information and semantic context to infer missing entities.<n>We propose SLiNT, a modular framework that injects knowledge-graph-derived structural context into a frozen backbone with lightweight LoRA-based adaptation for robust link prediction.<n>Experiments on WN18RR and FB15k-237 show that SLiNT achieves superior or competitive performance compared with both embedding-based and generation-based baselines.
  arXiv  Detail & Related papers  (2025-09-08T10:36:49Z)
• Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models [56.61984030508691]
  We present the first mechanistic interpretability study of language confusion.<n>We show that confusion points (CPs) are central to this phenomenon.<n>We show that editing a small set of critical neurons, identified via comparative analysis with a multilingual-tuned counterpart, substantially mitigates confusion.
  arXiv  Detail & Related papers  (2025-05-22T11:29:17Z)
• Learning from Neighbors: Category Extrapolation for Long-Tail Learning [62.30734737735273]
  We offer a novel perspective on long-tail learning, inspired by an observation: datasets with finer granularity tend to be less affected by data imbalance.<n>We introduce open-set auxiliary classes that are visually similar to existing ones, aiming to enhance representation learning for both head and tail classes.<n>To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss.
  arXiv  Detail & Related papers  (2024-10-21T13:06:21Z)
• Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
  We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing. We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM. We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
  arXiv  Detail & Related papers  (2024-10-01T04:20:14Z)
• Self-training Large Language Models through Knowledge Detection [26.831873737733737]
  Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. This paper explores a self-training paradigm, where the LLM autonomously curates its own labels and selectively trains on unknown data samples. Empirical evaluations demonstrate significant improvements in reducing hallucination in generation across multiple subjects.
  arXiv  Detail & Related papers  (2024-06-17T07:25:09Z)
• Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models [35.58379464827462]
  We introduce a post-training pruning strategy designed to faithfully replicate the embedding space and feature space of dense language models. Compared to other state-of-art baselines, our approach demonstrates a superior balance between accuracy, sparsity, robustness, and pruning cost with BERT on datasets SST2, IMDB, and AGNews.
  arXiv  Detail & Related papers  (2023-10-19T23:02:29Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Data Augmentation for Spoken Language Understanding via Pretrained Language Models [113.56329266325902]
  Training of spoken language understanding (SLU) models often faces the problem of data scarcity. We put forward a data augmentation method using pretrained language models to boost the variability and accuracy of generated utterances.
  arXiv  Detail & Related papers  (2020-04-29T04:07:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.