We've proposed Feature Selection Gates (FSG) — a lightweight, plug-in module that injects differentiable attention gates into Vision Transformers (ViT). It learns instance-specific token relevance and routes gradients accordingly, leading to sparser, more focused, and often more interpretable attention flows.
📄 Papers:
- Feature Selection Gates with Gradient Routing for Endoscopic Image Computing https://www.researchgate.net/publication/384576386_Feature_Selection_Gates_with_Gradient_Routing_for_Endoscopic_Image_Computing
- Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing https://www.researchgate.net/publication/382065314_Hard-Attention_Gates_with_Gradient_Routing_for_Endoscopic_Image_Computing
💻 Code (easy to integrate into ViT): https://github.com/cosmoimd/feature-selection-gates
1) Can Feature Selection Gates (FSG) be generalized as a token relevance mechanism across domains like object detection, action recognition, or RT-DETR pipelines—especially where attention efficiency, interpretability, or data constraints matter?
FSG acts as a learnable filtering mechanism on attention weights. Could this paradigm offer a new class of attention regularizers or gradient routers that:
- enhance data efficiency,
- reduce overhead in dense token maps (e.g. videos, long sequences),
- or guide attention toward semantically aligned regions (e.g. in detection or temporal reasoning)?
Looking forward to insights on use cases beyond medical imaging. Has anyone tried similar approaches in general vision tasks or Transformers beyond ViT?
This is a fascinating direction—especially the use of Feature Selection Gates (FSG) to enforce interpretable and sparse attention in ViTs. In our research on deep fake detection, we tackled a similar challenge from a different angle. Our framework employs Deep Q-Learning coupled with an Adaptive Genetic Tuned Optimization (AGTO) module to dynamically refine feature selection policies during training. While we didn’t use Transformers directly, our architecture similarly emphasizes efficient feature prioritization and adaptive routing of decision signals, particularly under constrained inference environments.
The idea of gradient-aware attention filtering resonates with our approach to learning optimal decision paths—in our case, through reinforcement learning signals guiding the detection pipeline toward key facial artifacts and inconsistencies. The concept of FSG as a lightweight gating layer could be highly applicable to video-based forgery detection, where token density is high, and temporal attention routing is critical for performance.
I’d be interested to explore whether FSG modules could be hybridized with RL-driven gating policies, especially for dynamic visual tasks like deep fake detection, action recognition, or spatio-temporal anomaly spotting.
Please check the below paper for more details
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