Abstract

In this paper, we propose a novel graph-based approach for image retrieval.

We propose a novel graph-based approach for image retrieval. Given a nearest neighbor graph produced by the global descriptor model, we traverse it by alternating between exploit and explore steps. The exploit step maximally utilizes the immediate neighborhood of each vertex, while the explore step traverses vertices that are farther away in the descriptor space. By combining these two steps we can better capture the underlying image manifold, and successfully retrieve relevant images that are visually dissimilar to the query. Our traversal algorithm is conceptually simple, has few tunable parameters and can be implemented with basic data structures. This enables fast real-time inference for previously unseen queries with minimal memory overhead. Despite relative simplicity, we show highly competitive results on multiple public benchmarks, including the largest image retrieval dataset that is currently publicly available.

Abstract

Semi-Supervised Exploration in Image Retrieval

We present our solution to Landmark Image Retrieval Challenge 2019. This challenge was based on the large Google Landmarks Dataset V2. The goal was to retrieve all database images containing the same landmark for every provided query image. Our solution is a combination of global and local models to form an initial KNN graph. We then use a novel extension of the recently proposed graph traversal method EGT referred to as semi-supervised EGT to refine the graph and retrieve better candidates.

➢DIR concatenated with GeM trained on Landmark-v1 dataset.

➢QE and spatial verification (RANSAC) using DELF-V2

➢Novel Contribution: Semi-supervised extension of EGT for final ranking.

Abstract

Released in 2016, the Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles. The answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Performance on SQuAD surpassed human performance in 2018, and in response the SQuAD2.0 challenge was released, which combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. Solutions to SQuAD2.0 will not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. The Layer 6 NLP Team developed a model that ranked the 2nd place in the leadership board (as of March 28, 2019).

Abstract

The paper presents a two-stage model to evaluate and advance current state-of-the-art in automated playlist continuation using a large scale dataset released by Spotify.

Automatic playlist continuation is a prominent problem in music recommendation. Significant portion of music consumption is now done online through playlists and playlist-like online radio stations. Manually compiling playlists for consumers is a highly time consuming task that is difficult to do at scale given the diversity of tastes and the large amount of musical content available. Consequently, automated playlist continuation has received increasing attention recently. The 2018 ACM RecSys Challenge is dedicated to evaluating and advancing current state-of-the-art in automated playlist continuation using a large scale dataset released by Spotify. In this paper we present our approach to this challenge.

We use a two-stage model where the first stage is optimized for fast retrieval, and the second stage re-ranks retrieved candidates maximizing the accuracy at the top of the recommended list. Our team vl6 achieved 1’st place in both main and creative tracks out of over 100 teams.

Abstract

Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease. In this challenge, we were challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Layer 6 collaborated with 16Bit and developed an ensemble of 15 state-of-the-art object detection models (10 Mask RCNN, 3 YOLOv3, and 2 Faster RCNN models), in combination with a classifier (DenseNet-121architecture pre-trained on NIH Chest X-rays data set) that served to reduce false positives, to detect pneumonia chest X-rays. We found that using a relaxed detection threshold for object detection, whilst requiring unanimous agreement among the detectors, effectively consolidated the need to minimize both false positives and false negatives. Adaptive histogram equalization was used to improve image contrast as a data preprocessing step. We used age, sex, and view position as inputs into the penultimate layer of the classifier to improve performance.