Name
Abstract | ||
|---|---|---|
Fine-grained object recognition is challenging due to large intra-class variation and inter-class ambiguity. A good algorithm should be able to: 1) discover discriminative local details and 2) align and aggregate these local discriminative patch-level features in an effective way to facilitate object level classification. Towards this end, we propose a novel local feature discovery, discriminative alignment and aggregation framework, inspired by the recent success of deep recurrent attention model. First, we develop a novel attribute-guided attentive network to sequentially discover informative parts/regions, by seeking a good registration between attentive regions and predefined object attributes. This could be considered as a semantic guided salient region discovery and alignment network, which might be more robust than conventional attention model. Second, these discovered regions are actively and progressively fed into a recurrent neural network, to yield the object-level representation. This could be considered as a discriminant aggregation network and informative patch-level features are propagated and accumulated to the deeper nodes of the recurrent network for final classification. We extensively test our framework on two fine-grained image benchmarks and the results demonstrate the effectiveness of the proposed framework.
|
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3123266.3123358 | MM '17: ACM Multimedia Conference
Mountain View
California
USA
October, 2017 |
Keywords | Field | DocType |
Fine-grained recognition, attribute guidance, active fusion | Computer science,Recurrent neural network,Attention model,Artificial intelligence,Ambiguity,Discriminative model,Computer vision,Pattern recognition,Feature aggregation,Feature discovery,Machine learning,Cognitive neuroscience of visual object recognition,Salient | Conference |
ISBN | Citations | PageRank |
978-1-4503-4906-2 | 3 | 0.36 |
References | Authors | |
22 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yichao Yan | 1 | 90 | 6.70 |
| Bingbing Ni | 2 | 1421 | 82.90 |
| Xiaokang Yang | 3 | 3581 | 238.09 |