ExchNet:A Unified Hashing Network for Large-Scale Fine-Grained Retrieval 193 Local Features 山 Similzr Codes Fig.3.Key idea of our local feature alignment approach:given an image pair of a fine-grained category,exchanging their local features of the same object parts should not change their corresponding hash codes,i.e.,these hash codes should be the same as those generated without local feature exchanging and their Hamming distance should be still close also. which tried to learn binary codes in the first stage and employed feature learning guided by the learned binary codes in the second stage.Then,there appeared numerous one-stage deep supervised hashing methods,including Deep Pairwise Supervised Hashing (DPSH)[17],Deep Supervised Hashing (DSH)[22],and Deep Cauchy Hashing (DCH)[3],which aimed to integrate feature learning and hash code learning into an end-to-end framework. 3 Methodology The framework of our ExchNet is presented in Fig.2,which contains three key modules,i.e.,the representation learning module,local feature alignment module,and hash code learning module. 3.1 Representation Learning The learning of discriminative and meaningful local features is mutually cor- related with fine-grained tasks 9,15,20,37,40],since these local features can greatly benefit the distinguishing of sub-categories with subtle visual differences deriving from the discriminative fine-grained parts(e.g.,bird heads or tails).In consequence,as shown in Fig.2,beyond the global feature extractor,we also introduce a local feature extractor in the representation learning module.Specif- ically,by considering model efficiency,we hereby propose to learn local features with the attention mechanism,rather than other fine-grained techniques with tremendous computation cost,e.g.,second-order representations [15,20]or com- plicated network architectures [9,37,40]. Given an input image xi,a backbone CNN is utilized to extract a holistic deep feature EE RHxWxC,which serves as the appetizer for both the local feature extractor and the global feature extractor.ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Retrieval 193 Fig. 3. Key idea of our local feature alignment approach: given an image pair of a fine-grained category, exchanging their local features of the same object parts should not change their corresponding hash codes, i.e., these hash codes should be the same as those generated without local feature exchanging and their Hamming distance should be still close also. which tried to learn binary codes in the first stage and employed feature learning guided by the learned binary codes in the second stage. Then, there appeared numerous one-stage deep supervised hashing methods, including Deep Pairwise Supervised Hashing (DPSH) [17], Deep Supervised Hashing (DSH) [22], and Deep Cauchy Hashing (DCH) [3], which aimed to integrate feature learning and hash code learning into an end-to-end framework. 3 Methodology The framework of our ExchNet is presented in Fig. 2, which contains three key modules, i.e., the representation learning module, local feature alignment module, and hash code learning module. 3.1 Representation Learning The learning of discriminative and meaningful local features is mutually cor￾related with fine-grained tasks [9,15,20,37,40], since these local features can greatly benefit the distinguishing of sub-categories with subtle visual differences deriving from the discriminative fine-grained parts (e.g., bird heads or tails). In consequence, as shown in Fig. 2, beyond the global feature extractor, we also introduce a local feature extractor in the representation learning module. Specif￾ically, by considering model efficiency, we hereby propose to learn local features with the attention mechanism, rather than other fine-grained techniques with tremendous computation cost, e.g., second-order representations [15,20] or com￾plicated network architectures [9,37,40]. Given an input image xi, a backbone CNN is utilized to extract a holistic deep feature Ei ∈ RH×W×C , which serves as the appetizer for both the local feature extractor and the global feature extractor