#EBAY SMART SEARCH SERIES#

The original embedding is modulated by element-wise multiplication with the selected mask, as shown on the right.

The mask is determined by the selected condition A or B as shown in the middle.

The original embedding is shown on the left. Values in the mask vector lie between 0 and 1 with a total sum of 1. Illustration of modulating an embedding by a conditional mask. This modulation operation is illustrated in Figure 2.įigure 2. The underlying vector is common for all conditions and is modulated by the mask vector by element-wise multiplication. CSN learns a mask vector for each condition. One way to achieve this is to use “ Conditional Similarity Networks” (CSN) which was published in CVPR 2017. Thus, there is a need that the mapping function adapts to the desired conditions or constraints. A single mapping function cannot be used to measure similarity universally. For example, as in Figure 1, we may be interested in measuring the similarity of images A, B, and C based on brand or based on buckles. If our vector is of dimension 1, as shown here, there is no way to capture both of these two conditions (brand, buckles) of similarity simultaneously. Similarly, pair (A, C) should be closer than (A, B) when we want to measure similarity based on presence on buckles. Pair (A, B) should be closer than (A, C) when we want to measure brand similarity. In the above example, products A, B have the same brand, and products A, C have buckles. Closer pairs are more similar than those farther apart. These vectors are used to measure similarity of pairs of images. Each image is mapped into a vector using a deep neural network. For example, in Figure 1, (A, B, C) is a valid triplet for brand and (A, C, B) is a valid triplet for fastening type.įigure 1. The definition of positive and negative samples could be based on whether they have a specific attribute in common or not. The resulting embedding is called “triplet embedding.” Valid triplets are constructed such that they are made up of three images (A, P, N), where A is the anchor, P is a positive sample, and N is a negative sample. One way to train such deep neural networks is to train it using triplets and predict if they have the desired order. the deep neural network maps the input image to a vector. This vector could be generated by a deep neural network, i.e. Similarity is then measured by proximity based on this vector.

#EBAY SMART SEARCH SERIES#

We represent each image by a compact signature in the form of a series of numbers represented as a vector a.k.a. Background: similarity using triplet embedding However, extension to personalization is straightforward, since our approach could be generalized easily. The scope of this article does not include personalization, but includes only user feedback during the search session for a single quest. Watch the 5-minute video at the end of this article as an alternative. More technical details can be found in our paper “ Give me a hint! Navigating Image Databases using Human-in-the-loop Feedback,” that was presented at WACV 2019. The aim of this material is to keep it simple. ScopeĪlthough the same could be true for text or speech or multimodal search, we limit our discourse to image queries. We present an approach for interactive visual search. Results on the next (unseen) page could be updated based on images that experienced a click-through. Such feedback could be based on items in the result set that had a click-through from the search result page. What if (i) the user knows the exact product but does not have the picture or (ii) the user has an image of a product but wants one with a few variations from the query or (iii) the user wants to explore the beautiful product landscape? An interactive approach is natural in such scenarios where the user gives feedback after each result set. It is still possible that the exact product is not in the result set for reasons such as product out of stock. This is great when the user has the picture of the exact product and finds the right match in the result set in terms of price, size, condition, shipping, etc. One could consider this as a single iteration of the search process. In our previous article, “ Seven Tips for Visual Search at Scale,” we discussed visual search where a user query is an image, and the results are shown to the user based on visual similarity to that query image.

Ebay smart search