arXiv Machine Learning June 25, 2026 By Lan Li, Tao Hu, Da-Wei Zhou, Jia-Qi Yang, Han-Jia Ye, De-Chuan Zhan

BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

Read the original on arXiv Machine Learning →

arXiv:2511. 11421v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

4d ago

Dual-Mode Low-Rank Learner with Bridge-Prototype Ensemble for Vision-Language Class-Incremental Learning

arXiv:2609.36759v1 Announce Type: cross Abstract: Benefiting from transferable visual-textual alignment, CLIP has been widely adopted for class-incremental learning (CIL). However, existing learners...

By Chiyuan He, Zihuan Qiu, Fanman Meng, Chao Wang, Liangjiang Chen, Linfeng Xu, Qingbo Wu, Hongliang Li

rag multimodal benchmarks safety

arXiv Machine Learning

Jun 16

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

arXiv:2606. 16256v1 Announce Type: cross Abstract: Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge.

By Mao-Lin Luo, Yi-Lin Zhang, Zi-Hao Zhou, Yankun Hong, Xialiang Tong, Mingxuan Yuan, Tong Wei, Min-Ling Zhang

llms computer-vision nlp fine-tuning multimodal

arXiv AI

6d ago

Prompt-Based Continual Compositional Zero-Shot Learning

The paper introduces PromptCCZSL, a framework that enables vision‑language models to continually learn new attributes, objects, and their unique compositions while avoiding forgetting. It uses a frozen VLM backbone with prompt‑based techniques, recency‑weighted multi‑teacher distillation, and several loss functions (CAL, OPL, IDL) to maintain prior knowledge and promote diverse, distinct embeddings. Experiments on UT‑Zappos and C‑GQA show significant performance gains over existing VLM‑based and non‑VLM baselines, establishing a new benchmark for continual compositional zero‑shot learning.

By Sauda Maryam, Sara Nadeem, Faisal Qureshi, Mohsen Ali

llms rag efficiency multimodal benchmarks

arXiv Machine Learning

Jul 2

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

arXiv:2602. 21397v2 Announce Type: replace-cross Abstract: Prompt learning has become a dominant paradigm for adapting vision-language models (VLMs) such as CLIP to downstream tasks without modifying pretrained weights.

By Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh, Ramtin Pedarsani

llms rag multimodal benchmarks safety

arXiv AI

Aug 25

TASSO: TAsk-Specific Subspace Optimization for Continual Learning of Vision-Language Models

arXiv:2608.21487v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) exhibit strong zero-shot capabilities, making them an attractive solution for continual learning across diverse tasks....

By Chang Sun, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh

llms rag diffusion efficiency multimodal benchmarks

arXiv Computer Vision

Sep 1

Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs

arXiv:2507.00754v3 Announce Type: replace Abstract: The integration of Large Language Model (LLMs) blocks with Vision Transformers (ViTs) holds immense promise for vision-only tasks by leveraging the...

By Selim Kuzucu, Muhammad Ferjad Naeem, Anna Kukleva, Federico Tombari, Bernt Schiele

llms fine-tuning

One email a morning, machine-written

Related stories

Dual-Mode Low-Rank Learner with Bridge-Prototype Ensemble for Vision-Language Class-Incremental Learning

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

Prompt-Based Continual Compositional Zero-Shot Learning

MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation

TASSO: TAsk-Specific Subspace Optimization for Continual Learning of Vision-Language Models

Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs