REVIEW 5 cited by
Compositional Few-Shot Class-Incremental Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient data. However, this remains a challenge. In contrast, humans can easily recognize novel classes with a few samples. Cognitive science demonstrates that an important component of such human capability is compositional learning. This involves identifying visual primitives from learned knowledge and then composing new concepts using these transferred primitives, making incremental learning both effective and interpretable. To imitate human compositional learning, we propose a cognitive-inspired method for the FSCIL task. We define and build a compositional model based on set similarities, and then equip it with a primitive composition module and a primitive reuse module. In the primitive composition module, we propose to utilize the Centered Kernel Alignment (CKA) similarity to approximate the similarity between primitive sets, allowing the training and evaluation based on primitive compositions. In the primitive reuse module, we enhance primitive reusability by classifying inputs based on primitives replaced with the closest primitives from other classes. Experiments on three datasets validate our method, showing it outperforms current state-of-the-art methods with improved interpretability. Our code is available at https://github.com/Zoilsen/Comp-FSCIL.
Forward citations
Cited by 5 Pith papers
-
Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning
LGSP-Prompt replaces token-dimension prompt pools with local and global spatial prompts, reporting state-of-the-art accuracy on CUB-200, FGVCAircraft, and iNF200 FSCIL benchmarks.
-
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
A residual adapter called DFN plus singular-value sharpness regularization improves cross-domain few-shot segmentation by 2.69% and 4.68% MIoU over prior state-of-the-art in 1-shot and 5-shot settings.
-
Revisiting Continuity of Image Tokens for Cross-domain Few-shot Learning
Disrupting the spatial continuity of image tokens hurts source-domain accuracy far more than target-domain accuracy, and using such disruption during source training improves cross-domain few-shot classification.
-
Random Registers for Cross-Domain Few-Shot Learning
Random registers in a vision transformer improve cross-domain few-shot transfer, and REAP strengthens this by replacing clustered image patches with random noise.
-
Free-Lunch Augmentation by Revisiting Diffusion-Based Data Generation for Cross-Domain Few-Shot Object Detection
SITN improves cross-domain few-shot detection and segmentation by using weakened-noise diffusion and background inpainting to synthesize helpful training images, outperforming prior methods on all reported benchmarks.
Discussion (0). Sign in to comment.