arXiv Computer Vision August 28, 2026 By Mingyu Wang, Wei Jiang, Haojie Liu, Zhiyong Li, Weijie Mao

Beyond Discrete Samples: High Information Density Replay for Efficient Lifelong Person Re-Identification

Read the original on arXiv Computer Vision →

The paper introduces HiDeR, a High Information Density Replay framework for Lifelong Person Re-Identification that replaces discrete sample selection with information compression. It uses a complexity‑aware memory allocation based on intra‑class variance and a metric‑guided condensation objective to preserve essential identity topologies, while a cross‑modality adaptation strategy bridges synthetic and real styles to improve training. Experiments show HiDeR outperforms state‑of‑the‑art methods in knowledge retention and generalization, and reduces cumulative replay cost.

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 Computer Vision.

benchmarks

arXiv Computation and Language

Aug 21

EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation

arXiv:2604. 26170v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge.

By Ting-Wei Li, Sirui Chen, Jiaru Zou, Yingbing Huang, Tianxin Wei, Jingrui He, Hanghang Tong

llms benchmarks safety

arXiv Machine Learning

Jun 8

CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations

arXiv:2606. 07488v1 Announce Type: new Abstract: Personalized virtual heart simulations face challenges in model personalization and computational cost.

By Ryan Missel, Xiajun Jiang, Linwei Wang

benchmarks

arXiv Machine Learning

Jul 31

Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting

arXiv:2508. 04227v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerful cross-modal alignment and zero-shot generalization.

By Yuyang Liu, Qiuhe Hong, Linlan Huang, Alexandra Gomez-Villa, Dipam Goswami, Tiantian Peng, Xialei Liu, Joost van de Weijer, Yonghong Tian

llms agents robotics multimodal benchmarks safety

arXiv AI

Jul 15

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.

By Jing Liu, Chenxuanyin Zou, Jiayang Ren, Gaoyun Fang, Chengfang Li, Yan Wang, Zhenchao Ma, Bo Hu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv Computer Vision

1d ago

RECAP-Forcing: Retaining Content Appearances for Long Video Generation

arXiv:2608.26671v1 Announce Type: new Abstract: Long autoregressive video generation faces a fundamental memory challenge: with a finite attention window, a model must decide which information from a...

By Haiyang Xu, Zheng Ding, Zhuowen Tu

diffusion efficiency

arXiv Computer Vision

1d ago

Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion

arXiv:2608.26794v1 Announce Type: new Abstract: Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied a...

By Bowen Xue, Brandon Y. Feng, Chenguo Lin, Yuchen Lin, Yujia Zeng, Lvmin Zhang, Maneesh Agrawala, Honglei Yan, Panwang Pan

diffusion benchmarks