- 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
- ML-Decoder: Scalable and Versatile Classification Head | IEEE . . .
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 be
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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
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