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Found 6 papers, 4 papers with code
Knowledge Graph Driven UAV Cognitive Semantic Communication Systems for Efficient Object Detection
Unmanned aerial vehicles (UAVs) are widely used for object detection.
GIFT: Generative Interpretable Fine-Tuning
We show the output of the first linear layer (i. e., $\omega\cdot \phi$) is surprisingly interpretable, which can play the role of a token-clustering head as a by-product to localize meaningful objects/parts in images for computer vision tasks.
PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers
The proposed PaCa module is used in designing efficient and interpretable ViT backbones and semantic segmentation head networks.
AOGNets: Compositional Grammatical Architectures for Deep Learning
This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs.
Towards Interpretable R-CNN by Unfolding Latent Structures
We focus on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations.
Discriminatively Trained And-Or Tree Models for Object Detection
This paper presents a method of learning reconfigurable And-Or Tree (AOT) models discriminatively from weakly annotated data for object detection.
