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ML-Decoder: Scalable and Versatile Classification Head In this paper, we introduce ML-Decoder, a new attention-based classification head ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling
ML-Decoder: Scalable and Versatile Classification Head In this paper, we introduced ML-Decoder, a new attention-based classification head By removing the redun-dant self-attention layer and using a novel group-decoding scheme, ML-Decoder scales well to thousands of classes, and provides a better speed-accuracy trade-off
GitHub Compared to using a larger backbone, ML-Decoder consistently provides a better speed-accuracy trade-off \nML-Decoder is also versatile - it can be used as a drop-in replacement for various classification heads, and generalize to unseen classes when operated with word queries
ML-Decoder: Scalable and Versatile Classification Head In this paper, we introduce ML-Decoder, a new attention-based classification head ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data
ML-Decoder: Scalable and Versatile Classification Head L-Decoder consistently provides a better speed-accuracy trade-off ML-Decoder is also versatile - it can be used as a drop-in replacement for various classification heads
ML-Decoder: Scalable and Versatile Classification Head In this paper, we introduce ML-Decoder, a new attention-based classification head ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling
ML-Decoder: Scalable and Versatile Classification Head ML-Decoder: Scalable and Versatile Classification Head: Paper and Code In this paper, we introduce ML-Decoder, a new attention-based classification head ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling
ML-Decoder: Scalable and Versatile Classification Head In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations
ML-Decoder: Scalable and Versatile Classification Head | Cool Papers . . . In this paper, we introduce ML-Decoder, a new attention-based classification head ML-Decoder predicts the existence of class labels via queries, and enables better utilization of spatial data compared to global average pooling