12 research outputs found
Why do These Match? Explaining the Behavior of Image Similarity Models
Explaining a deep learning model can help users understand its behavior and
allow researchers to discern its shortcomings. Recent work has primarily
focused on explaining models for tasks like image classification or visual
question answering. In this paper, we introduce Salient Attributes for Network
Explanation (SANE) to explain image similarity models, where a model's output
is a score measuring the similarity of two inputs rather than a classification
score. In this task, an explanation depends on both of the input images, so
standard methods do not apply. Our SANE explanations pairs a saliency map
identifying important image regions with an attribute that best explains the
match. We find that our explanations provide additional information not
typically captured by saliency maps alone, and can also improve performance on
the classic task of attribute recognition. Our approach's ability to generalize
is demonstrated on two datasets from diverse domains, Polyvore Outfits and
Animals with Attributes 2. Code available at:
https://github.com/VisionLearningGroup/SANEComment: Accepted at ECCV 202
Attribute disentanglement with gradient reversal for interactive fashion retrieval
Interactive fashion search is gaining more and more interest thanks to the rapid diffusion of online retailers. It allows users to browse fashion items and perform attribute manipulations, modifying parts or details of given garments. To successfully model and analyze garments at such a fine-grained level, it is necessary to obtain attribute-wise representations, separating information relative to different characteristics. In this work we propose an attribute disentanglement method based on attribute classifiers and the usage of gradient reversal layers. This combination allows us to learn attribute-specific features, removing unwanted details from each representation. We test the effectiveness of our learned features in a fashion attribute manipulation task, obtaining state of the art results. Furthermore, to favor training stability we present a novel loss balancing approach, preventing reversed losses to diverge during the optimization process
MMFL-Net: Multi-scale and Multi-granularity Feature Learning for Cross-domain Fashion Retrieval
Instance-level image retrieval in fashion is a challenging issue owing to its
increasing importance in real-scenario visual fashion search. Cross-domain
fashion retrieval aims to match the unconstrained customer images as queries
for photographs provided by retailers; however, it is a difficult task due to a
wide range of consumer-to-shop (C2S) domain discrepancies and also considering
that clothing image is vulnerable to various non-rigid deformations. To this
end, we propose a novel multi-scale and multi-granularity feature learning
network (MMFL-Net), which can jointly learn global-local aggregation feature
representations of clothing images in a unified framework, aiming to train a
cross-domain model for C2S fashion visual similarity. First, a new
semantic-spatial feature fusion part is designed to bridge the semantic-spatial
gap by applying top-down and bottom-up bidirectional multi-scale feature
fusion. Next, a multi-branch deep network architecture is introduced to capture
global salient, part-informed, and local detailed information, and extracting
robust and discrimination feature embedding by integrating the similarity
learning of coarse-to-fine embedding with the multiple granularities. Finally,
the improved trihard loss, center loss, and multi-task classification loss are
adopted for our MMFL-Net, which can jointly optimize intra-class and
inter-class distance and thus explicitly improve intra-class compactness and
inter-class discriminability between its visual representations for feature
learning. Furthermore, our proposed model also combines the multi-task
attribute recognition and classification module with multi-label semantic
attributes and product ID labels. Experimental results demonstrate that our
proposed MMFL-Net achieves significant improvement over the state-of-the-art
methods on the two datasets, DeepFashion-C2S and Street2Shop.Comment: 27 pages, 12 figures, Published by <Multimedia Tools and
Applications