Current few-shot action recognition methods achieve impressive performance by learning representative prototypes and designing diverse video matching strategies. However, these approaches typically face two critical limitations: i) prototypes learned through implicit sample interactions lack clear semantic correspondence between query-support pairs, limiting their class representativeness; ii) the independent design of prototype learning and matching mechanisms creates a potential incompatibility between prototype representations and matching strategies. To address these limitations, we propose a Match-guided Prototype Learning (MPL) method comprising two key components: enhanced match (E-Match) and key-frame extraction match (K-Match). E-Match explicitly enhances prototype learning in class-specific embeddings by incorporating the matched semantics of query samples, while K-Match further refines the prototype representation through key-frame matching at the fine-grained frame level. Additionally, we propose a Cross-Shot Attention Aggregator (CSA-Aggregator) that dynamically aggregates adjacent frames across support samples, thereby obtaining a prototype representation that captures intra-class shared action patterns. In this way, the proposed MPL effectively mines coarse-to-fine, match-guided semantic information from query-support pairs to generate discriminative class prototypes, and improve the compatibility of prototype representation with the match mechanism. Extensive evaluations on four public datasets confirm that MPL achieves superior performance over leading few-shot action recognition techniques. The source code is available at https://github.com/jayzh-research/MPL-FSAR.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
The increasing complexity of real-world deployment requires intelligent agents to effectively adapt to non-stationary data streams with stochastic increments under data scarcity. We formally define this challenge as the Few-Shot Hybrid Incremental Learning (FSHIL) paradigm, which reveals a critical stability-plasticity dilemma. Existing strategies struggle to address this dilemma: representation freezing in few-shot incremental learning can mitigate overfitting under data scarcity but leads to insufficient representation plasticity, while architecture expansion in hybrid incremental learning provides plasticity for adaptation but results in overfitting under few-shot conditions. To address this, we propose the Conditional Meta-Expanding Mixture-of-Experts (CME-MoE), which balances feature-level stability-plasticity trade-off through conditional expert reuse and meta-expansion mechanism. Furthermore, recognizing the multi-domain manifestation in the latent space, we introduce the Self-Expanding Prototype Classifier (SEPC), which on-demand expands classification to model complex domain-shifted decision boundaries. The proposed method outperforms existing state-of-the-art methods in three few-shot incremental learning settings across five mainstream datasets, effectively addressing data scarcity and task uncertainty, and providing a robust solution for real-world continual learning.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Xie, Xingxing, Dong, Jiahua, Han, Junwei, Cheng, Gong
YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image in every epoch, even when many images have already been sufficiently learned. This stands in clear contrast to the efficiency suggested by the "You Only Look Once" philosophy. This naturally raises an important question: Does YOLO really need to see every training image in every epoch? To explore this, we propose an Anti-Forgetting Sampling Strategy (AFSS) that dynamically determines which images should be used and which can be skipped during each epoch, allowing the detector to learn more effectively and efficiently. Specifically, AFSS measures the learning sufficiency of each training image as the minimum of its detection recall and precision, and dynamically categorizes training images into easy, medium, or hard levels accordingly. Easy training images are sparsely resampled during training in a continuous review manner, with priority given to those that have not been used for a long time to reduce redundancy and prevent forgetting. Moderate training images are partially selected, prioritizing recently unused ones and randomly choosing the rest from unselected images to ensure coverage and prevent forgetting. Hard training images are fully sampled in every epoch to ensure sufficient learning. The learning sufficiency of each training image is periodically updated, enabling detectors to adaptively shift its focus toward the informative training images over time while progressively discarding redundant ones. On widely used natural image detection benchmarks (MS COCO 2017 and PASCAL VOC 2007) and remote sensing detection datasets (DOTA-v1.0 and DIOR-R), AFSS achieves more than 1.43x training speedup for YOLO-series detectors while also improving accuracy.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表