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.
arXiv:2603. 12055v3 Announce Type: replace-cross Abstract: Continual learning of pretrained vision-language models (VLMs) is prone to catastrophic forgetting, yet current approaches adapt to new tasks without explicitly preserving the cross-modal semantic geometry inherited from pretraining and previous stages, allowing new-task supervision to induce geometric distortion.
arXiv:2607. 14514v1 Announce Type: cross Abstract: Object-goal navigation requires an embodied agent to locate and reach an instance of a specified object category in an indoor environment.
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.
IntactWorld introduces a Joint World Modeling Architecture that preserves uncompressed Intact Features to better capture real‑world logic in video generation. By predicting clean intermediate features instead of flow velocity in a high‑dimensional space, it avoids manifold gaps and reduces computational overhead through a Full‑to‑Compact Training Paradigm that replaces raw features with refined CLS tokens. The approach cuts spatial memory usage by 11.4% and inference latency by 43.8%, achieving a 2.46‑point improvement on the VBench 2.0 benchmark.
arXiv:2607. 09785v1 Announce Type: cross Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams.
arXiv:2606. 30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.
arXiv:2606. 31275v1 Announce Type: cross Abstract: Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints.
arXiv:2510.21175v2 Announce Type: replace Abstract: Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated remarkable zero-shot generalization, enabling deployment in a wide range...
arXiv:2604.03212v4 Announce Type: replace Abstract: Remote sensing segmentation in real deployment is inherently continual: new semantic categories emerge, and acquisition conditions shift across sea...
arXiv:2606. 29763v1 Announce Type: cross Abstract: Topological data analysis (TDA), particularly persistent homology (PH), captures geometric structural properties in medical images (e.
arXiv:2606. 11576v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual contexts and long decoding chains.
arXiv:2607. 25531v1 Announce Type: cross Abstract: Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure.