RegCL is a non‑replay continual learning framework that adapts the Segment Anything Model (SAM) for visual grounding across evolving multi‑sensorial media domains. It consolidates domain‑specific segmentation knowledge into a single lightweight SAM adapter by incrementally merging LoRA‑style AugModules and preserving compact historical feature statistics. Experiments on five heterogeneous datasets demonstrate that RegCL retains performance while adapting to new domains, outperforming other non‑replay continual learning and merging baselines.
By Yuan-Chen Shu, Zhiwei Lin, Xiaoyu Zhou, Yongtao Wang
arXiv:2603. 09420v3 Announce Type: replace-cross Abstract: Motion forecasting enables autonomous vehicles to anticipate scene evolution by predicting the future trajectories of dynamic agents.
By Nicolas Schischka, Nikhil Gosala, B Ravi Kiran, Senthil Yogamani, Abhinav Valada
arXiv:2606. 05695v1 Announce Type: new Abstract: Exemplar-free class-incremental learning (EFCIL) aims to acquire new classes over time without storing raw data.
By Hongye Xu, Bartosz Krawczyk
Prompt-driven vision-language models (VLMs) hold immense promise for accelerating dense remote sensing (RS) annotation, but static models suffer from severe performance degradation when deployed on novel scenes, unseen categories, or visually confusing backgrounds. Moreover, existing unified paradigms primarily rely on intra-image specific prompts, lacking flexible task routing to adapt to multi-intent operational workflows.
Hyperbolic Prototype Routing (HyPro) is a rehearsal‑free framework for class‑incremental learning that assigns a dedicated LoRA‑Expert module to each incremental task, enabling isolated representation learning. It projects routing features onto a Poincaré ball and uses geodesic nearest‑prototype matching for reliable task‑level discrimination. Experiments on standard and few‑shot CIL benchmarks demonstrate that HyPro consistently improves both average and final‑stage accuracy compared to strong baselines.
By HongWei Zhao (Beihang University), Rui Liu (Beihang University), Yong Chen (Beijing University of Posts,Telecommunications)
arXiv:2609.18444v1 Announce Type: new
Abstract: Weakly supervised class-incremental semantic segmentation (WILSS) aims to train a segmentation model over multiple steps, each introducing new concepts...
By Leon Arthur Marx, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
arXiv:2607. 15768v1 Announce Type: cross Abstract: Remote sensing offers an unparalleled vantage point for observing the Earth's long-term surface evolution, yet it demands that a model not only perceive land cover at isolated moments, but also track changes, memorize evolution histories, and reason across time and space.
By Yujie Li, Jiancheng Pan, Zhiwei Wei, Jiuniu Wang, Mugen Peng, Wenjia Xu
arXiv:2601. 22012v3 Announce Type: replace Abstract: Catastrophic forgetting in continual learning is often measured at the performance or last-layer representation level, overlooking the underlying mechanisms.
By Sergi Masip, Gido M. van de Ven, Javier Ferrando, Tinne Tuytelaars
The paper introduces Online Versatile Incremental Learning (Online VIL), a scenario where both class concepts and visual domains evolve simultaneously without explicit boundaries. To address this challenge, the authors propose TopFlow, a framework that combines Domain‑agnostic Flow Matching (DFM) and Global Topology Preservation (GTP) to maintain domain‑agnostic representations and the global structure of the feature space without storing past examples. Experiments show that TopFlow outperforms existing methods in the Online VIL setting, offering a promising direction for building continual learning systems in realistic dynamic environments.
By Jae-Ho Lee, Min-Yeong Park, Jun-Yeong Moon, Jung Uk Kim, Gyeong-Moon Park
LT-Mem introduces a volatility‑aware memory evolution framework for lifelong scene understanding, combining spatially aligned instance‑level 3D perception with temporal reasoning. It uses a multi‑session SLAM backbone, a reasoning layer that scores evidence and selects memory actions, and a Tri‑Memory structure (Live, Delta, Meta) to preserve current states and event histories. The accompanying LT‑VQA dataset provides multi‑session recordings, persistent identity annotations, and temporal QA pairs, and experiments show LT‑Mem outperforms baselines while using far fewer tokens.
arXiv:2606. 09430v1 Announce Type: cross Abstract: Online task-free continual learning (TFCL) requires intelligent agents to sequentially accumulate knowledge from an unbounded, non-stationary data stream under strict single-pass constraints and without any explicit task identifiers.
By Mingqi Yuan, Xiaoquan Sun, Shihao Luo, Jiayu Chen
arXiv:2606. 09960v1 Announce Type: cross Abstract: We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedded and resource-constrained environments.
By Daniel Vila-Cruz, Laura Mor\'an-Fern\'andez, Ver\'onica Bol\'on-Canedo