Prioritized offline Goal-swapping Experience Replay

In goal-conditioned offline reinforcement learning, an agent learns from previously collected data to go to an arbitrary goal. Since the offline data only contains a finite number of trajectories, a main challenge is how to generate more data. Goal-swapping generates additional data by switching trajectory goals but while doing so produces a large number of invalid trajectories. To address this issue, we propose prioritized goal-swapping experience replay (PGSER). PGSER uses a pre-trained Q function to assign higher priority weights to goal swapped transitions that allow reaching the goal. In experiments, PGSER significantly improves over baselines in a wide range of benchmark tasks, including challenging previously unsuccessful dexterous in-hand manipulation tasks

VAASAN KAUPUNGINHALLITUKSEN KOKOUSTEN VIITTOMAKIELINEN UUTISOINTI

Opinnäytetyön tavoitteena oli videoida ja editoida valmis tuote Vaasan kaupunginhallituksen viittomankielisistä uutisista. Projektin tarkoitus oli parantaa ja tasavertaistaa viittomakielen asemaa suomen- ja ruotsinkielen kanssa Vaasan kaupungin tiedottamisessa. Vaasan alueella elää noin 300 kuulovammaista ihmistä. Videoiden avulla voidaan saada tieto kaupunginhallituksen päätöksistä heidän omalla äidinkielellään. Videointi tapahtui aluksi kaupungin omissa tiloissa, mutta tilojen valotus- ja ääniongelmien vuoksi aloimme heti ensimmäisen kuvauskerran jälkeen suunnitella aivan uutta kuvaustilaa. Uudeksi kuvaustilaksi vakiintuikin pieni kokoustila, joka sijaitsi Vaasan ammattiopiston (Silverian) tiloissa. Kuvaamiseen käytimme Vaasan ammattikorkeakoulun kameroita. Kamerat olivat malliltaan Sony HVR-Z1U HDV 1080i MiniDV Digital Video Camera. Editointi suoritettiin Vaasan ammattikorkeakoulun luokkatiloissa. Editointia varten käytössämme oli ammattilaiskäyttöä varten suunniteltu Adobe Premiere Pro CS6 videoeditointiohjelma. Valmiit videot ladattiin Youtubeen katseltavaksi. Videot osoittautuivat yllättävän suosituiksi, sillä jo toisella julkaistulla videolla oli ensimmäisen viikon jälkeen yli 200 näyttökertaa sekä katsojilta tullut suullinen palaute oli todella positiivista. Opinnäytteen tekemiseen käytin toiminnallisen opinnäytetyön menetelmää.The idea of the thesis project was to make sign language news videos about the board decisions of the city of Vaasa. There are about 300 persons with disabilities in hearing living in the Vaasa region. The videos produced help them get information in their own language. The videos were initially shot at municipal locations with poor light and sound conditions. Therefore a change of location to the Silveria Vocational School was made. All subsequent shootings were conducted from these facilities. For recording, the cameras of Vaasa University of Applied Sciences were used. The model used was Sony HVR-Z1U HDV 1080i MiniDV Digital Video Camera. Editing was carried out in Vaasa University of Applied Sciences class facilities. For editing Adobe Premiere Pro CS6 video editing software was used. The software is designed for professional use. The videos produces were uploaded and published on Youtube. The videos proved to be surprisingly popular, as already the second publication of the video got more than 200 views in a week

Anthropometric clothing measurements from 3D body scans

We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid Iterative Closest Point (ICP) algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a non-linear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects and the proposed pipeline provides mean absolute errors from 2.5 mm to 16.0 mm depending on the anthropometric measurement

Fast Fourier Intrinsic Network

We address the problem of decomposing an image into albedo and shading. We propose the Fast Fourier Intrinsic Network, FFI-Net in short, that operates in the spectral domain, splitting the input into several spectral bands. Weights in FFI-Net are optimized in the spectral domain, allowing faster convergence to a lower error. FFI-Net is lightweight and does not need auxiliary networks for training. The network is trained end-to-end with a novel spectral loss which measures the global distance between the network prediction and corresponding ground truth. FFI-Net achieves state-of-the-art performance on MPI-Sintel, MIT Intrinsic, and IIW datasets.Comment: WACV 2021 - camera read

Single Pixel Spectral Color Constancy

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