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Continual Visual Learning under Evolving Semantic Concept Shift
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arXiv Computer Vision
Aug 26
Continual Visual Learning under Evolving Semantic Concept Shift
arXiv:2608.23903v1 Announce Type: new Abstract: Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the pr...
arXiv Machine Learning
Jul 31
Continual Learning with Vision-Language Models via Semantic-Geometry Preservation
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 Machine Learning
Oct 21
Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning
The paper introduces HASTEN, a hierarchical semantic tree anchoring framework that enhances CLIP-based class‑incremental learning by incorporating external hierarchical knowledge into hyperbolic space. By treating classes as part of a structured hierarchy rather than isolated labels, HASTEN stabilizes visual and textual feature mappings, reduces feature drift, and mitigates catastrophic forgetting across incremental tasks. Experiments on nine benchmarks demonstrate that HASTEN consistently outperforms existing methods.
arXiv Computer Vision
4d ago
Semantic Concept Steering Breaks the Explanation Drift Loop in Continual Learning
arXiv:2610.33877v1 Announce Type: new Abstract: Catastrophic forgetting in continual learning (CL) manifests not only as accuracy degradation but also as \emph{explanation drift}: models maintain pre...
arXiv Machine Learning
Jul 28
EditCLEVR: A Paired-Scene Intervention Benchmark for Compositional Faithfulness of Object-Centric Representations
arXiv:2607. 22705v1 Announce Type: cross Abstract: Object-centric learning aims to represent scenes as objects whose properties can be reused in new combinations.
arXiv Machine Learning
Oct 9
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
The paper introduces a concept-guided poisoning unlearning framework that explicitly identifies semantic concepts most strongly linked to a target class or knowledge. For image classification, it uses a Post-hoc Concept Bottleneck Model to locate relevant image regions and creates poisoned samples; for large language models, it aggregates Integrated Gradients over multiple question responses to mask important content and generate poisoned training targets. Experiments across several datasets demonstrate effective unlearning while largely preserving model utility.