Webtempted to alleviate long-tailed problem by compensating the tail data [41,43,44]. Although they can treat the head and tail data equally, these methods may by easily affected by … Web22 de fev. de 2024 · DOI: 10.1109/CVPR52688.2024.00683 Corpus ID: 247058346; Retrieval Augmented Classification for Long-Tail Visual Recognition @article{Long2024RetrievalAC, title={Retrieval Augmented Classification for Long-Tail Visual Recognition}, author={Alex Long and Wei Yin and Thalaiyasingam Ajanthan and …

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WebIn this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes’ few-shot problem via training-free knowledge transfer. Our objective is to … Web16 de mai. de 2024 · In this paper, we tackle the long-tailed visual recognition problem from the categorical prototype perspective by proposing a prototype-based classifier learning (PCL) method. Specifically, thanks to the generalization ability and robustness, categorical prototypes reveal their advantages of representing the category semantics. Coupled with … kroger conway arkansas salem road

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WebAlmost all long-tailed methods perform better than the Softmax baseline in terms of accuracy, which demonstrates the effectiveness of long-tailed learning. Training with … WebA straightforward solution to long-tail object detection is to train a well-established detection model (e.g., Faster R-CNN [31]) on the long-tail training data directly. How-ever, big performance drop would be observed when adapt-ing detectors designed for fairly balanced datasets (e.g., COCO) to a long-tail one (e.g., LVIS), for which the rea ... Web3 de mar. de 2024 · For long-tail recognition, there have been numerous. works [8, 18, 26, 47] to obtain a balanced representation. space by introducing a contrastive loss. However, they usu-ally require a multi ... kroger conyers ga hwy 20