Learning Deep Representations of Fine-grained Visual Descriptions

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually encoded vectors describing shared characteristics among categories... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Few-Shot Image Classification CUB-200-2011 - 0-Shot Word CNN-RNN (DS-SJE Embedding) Top-1 Accuracy 56.8% # 1
AP50 48.7 # 1
Few-Shot Image Classification CUB 200 50-way (0-shot) DA-SJE Reed et al. (2016) Accuracy 50.9 # 2
Few-Shot Image Classification CUB 200 50-way (0-shot) DS-SJE Reed et al. (2016) Accuracy 50.4 # 3
Few-Shot Image Classification Flowers-102 - 0-Shot Word CNN-RNN (DS-SJE Embedding) AP50 59.6 # 1
Accuracy 65.6% # 1

Methods used in the Paper


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