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20242026
most citedContinual Forgetting for Pre-trained Vision Models

1 citations · 2 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

CL-VISTA: Benchmarking Continual Learning in Video Large Language Models

Haiyang Guo, Yichen Shi, Fei Zhu +6

Video Large Language Models (Video-LLMs) require continual learning to adapt to non-stationary real-world data. However, existing benchmarks fall short of evaluating modern foundat…

cs.CV2025

VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?

Hongbo Zhao, Meng Wang, Fei Zhu +5

The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC)…

cs.CV2025

MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark

Haiyang Guo, Fei Zhu, Hongbo Zhao +5

Continual learning enables AI systems to acquire new knowledge while retaining previously learned information. While traditional unimodal methods have made progress, the rise of Mu…

cs.CV2025

Practical Continual Forgetting for Pre-trained Vision Models

Hongbo Zhao, Fei Zhu, Bolin Ni +3

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests ori…

cs.CV2024

OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction

Hongbo Zhao, Lue Fan, Yuntao Chen +6

In this paper, we propose OpenSatMap, a fine-grained, high-resolution satellite dataset for large-scale map construction. Map construction is one of the foundations of the transpor…

cs.CV2024★ 1 cited

Enhancing Visual Continual Learning with Language-Guided Supervision

Bolin Ni, Hongbo Zhao, Chenghao Zhang +4

Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures,…

cs.CV2024★ 1 cited

Continual Forgetting for Pre-trained Vision Models

Hongbo Zhao, Bolin Ni, Haochen Wang +6

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests ori…