The Challenge (2016) LINK

Unlike the previous two installments of the Rivals series, which featured same-gender pairs, Rivals III saw a change in format, with male/female pairs that had bitter feuds, fights, or strained relationships in previous seasons of Real World, The Challenge, and Are You the One? (similar to the Battle of the Exes series). Unlike the other Rivals seasons, only the winning team from each week's challenge was allowed to decide which team would face the last-place team in the elimination round. The season premiered with a special 90 minutes episode on May 4, 2016,[1] and concluded its run on August 3, 2016, with the Reunion special.[2]

The Challenge (2016)

Prior to the final challenge, T. J. Lavin announced that not only would each team be competing against the other teams, but they would also have to compete against their fellow team members through a series of checkpoints. Each in-team victor would eventually be presented with an ultimate choice at the game's end: split the money with his/her partner, or take it all for himself/herself.[39]

The comprehensive gist of this challenge was pretty self-explanatory: put on 100 layers of anything. From 100 layers of nail polish to socks, this challenge really inspired a range of hilarious videos.

Oh, the mannequin challenge - the defining challenge of the year. From family parties to sold-out arenas, no social event was safe from a mannequin challenge video. In case you've already suppressed all memory from 2016, in this challenge, participants stand completely still (like mannequins) while a camera pans around them and "Black Beatles" by Rae Sremmurd ft. Gucci Mane plays.

This might have been our favorite challenge of the year because it made no sense whatsoever. In this challenge, one brave person runs between two rows of lined-up participants, throwing their backpacks full-force at the runner.

So, this challenge didn't fully catch on, but it brought us so much joy, we just couldn't leave it out. Inspired by Instagram user i_got_barzz(Opens in a new tab)'s dance video, The Shade Room(Opens in a new tab) attempted to launch a challenge where participants recreate the hilarious dance to the song "My Friends," by Mr. Hotspot.

The Read Harder group on Goodreads is also an excellent resource throughout the year for sharing your reading plans, discussing the tasks and finding new books to fit the challenge. Or check out a Read Harder Book Group in person! And check in all over social media with the hashtag #ReadHarder.

The ActivityNet Large Scale Activity Recognition Challenge is a half-day workshop to be held on July 1 in conjuction with CVPR 2016, in Las Vegas, Nevada. In this workshop, we establish a new challenge to stimulate the computer vision community to develop new algorithms and techinques that improve the state-of-the-art in human activity understanding. The data of this challenge is based on the newly published ActivityNet benchmark.

The challenge focuses on recognizing high-level and goal oriented activities from user generated videos, similar to those found in internet portals. This challenge is tailored to 200 activity categories in two different tasks. (a) Untrimmed Classification Challenge: Given a long video, predict the labels of the activities present in the video; (b) Detection Challenge: Given a long video, predict the labels and temporal extents of the activities present in the video.

NOTICE FOR PARTICIPANTS: In the challenge, you could use any pre-trained models as the initialization, but you need to write in the description which models have been used. There is only "provided data" track for the scene parsing challenge at ILSVRC'16, which means that you can only use the images and annotations provided and you cannot use any other images or segmentation annotations, such as Pascal or CityScapes. In the testing images, scene labels will not be provided.

For each image, algorithms will produce a set of annotations $(c_i, s_i, b_i)$ of class labels $c_i$, confidence scores $s_i$ and bounding boxes $b_i$. This set is expected to contain each instance of each of the 200 object categories. Objects which were not annotated will be penalized, as will be duplicate detections (two annotations for the same object instance). The winner of the detection challenge will be the team which achieves first place accuracy on the most object categories.

For each video clip, algorithms will produce a set of annotations $(f_i, c_i, s_i, b_i)$ of frame number $f_i$, class labels $c_i$, confidence scores $s_i$ and bounding boxes $b_i$. This set is expected to contain each instance of each of the 30 object categories at each frame. The evaluation metric is the same as for the objct detection task, meaning objects which are not annotated will be penalized, as will duplicate detections (two annotations for the same object instance). The winner of the detection from video challenge will be the team which achieves best accuracy on the most object categories.

This challenge is being organized by the MIT Places team, namely Bolei Zhou, Aditya Khosla, Antonio Torralba and Aude Oliva. Please feel free to send any questions or comments to Bolei Zhou (bzhou@csail.mit.edu).

The goal of this challenge is to identify the scene category depicted in a photograph. The data for this task comes from the Places2 Database which contains 10+ million images belonging to 400+ unique scene categories. Specifically, the challenge data will be divided into 8M images for training, 36K images for validation and 328K images for testing coming from 365 scene categories. Note that there is a non-uniform distribution of images per category for training, ranging from 3,000 to 40,000, mimicking a more natural frequency of occurrence of the scene.

This challenge is being organized by the MIT CSAIL Vision Group. The goal of this challenge is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bed. The data for this challenge comes from ADE20K Dataset (The full dataset will be released after the challenge) which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the challenge data is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing. There are totally 150 semantic categories included in the challenge for evaluation, which include stuffs like sky, road, grass, and discrete objects like person, car, bed. Note that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene.

Public evaluations are common in many areas of research, with some challenges being active for many consecutive years. They help push the boundaries of algorithm developments to deal with more and more complex tasks. TRECVID Multimedia Event detection is another of the long tradition evaluations, with focus on audiovisual, multi-modal event detection in video recordings. Such public evaluations provide a good opportunity for code dissemination, unification and definition of terms, procedures, benchmark datasets and evaluation metrics. It is our wish to provide a similar tool for computational auditory scene analysis, specifically for detection and classification of sound scenes and events.

The previously organized DCASE2013 challenge (sponsored by the IEEE AASP TC, and held at WASPAA 2013) attracted the interest of the research community and had a good participation rate. It also contributed on creating benchmark datasets and fostered reproducible research (6 out of 18 participating teams had their source code released through the challenge). Based on its success, we propose to organize the follow-up challenge on the performance evaluation of systems for the detection and classification of sound events. This challenge will move the DCASE setup closer to real world applications, by providing more complex problems. This will help defining a common ground for researchers that actively pursue research on this field, and offer a reference point for systems developed to perform parts of this task.

The goal of acoustic scene classification is to classify a test recording into one of predefined classes that characterizes the environment in which it was recorded -- for example "park", "street", "office". The acoustic data will include recordings from 15 contexts, approximately one hour of data from each context. The setup is similar to the previous DCASE challenge, but with a higher number of classes and diversity of data.

The sound event detection challenge will consist of 2 distinct tasks. This task will focus on event detection of office sounds, and will use training material provided as isolated sound events for each class, and synthetic mixtures of the same examples in multiple SNR and event density conditions (sounds have been recorded at IRCCYN, École Centrale de Nantes). The participants will be allowed to use any combination of them for training their system. The test data will consist of synthetic mixtures of (source-independent) sound examples at various SNR levels, event density conditions and polyphony. Thus, the aim of this task is to study the behaviour of tested algorithms when facing different levels of complexity, with an added benefit that the ground truth will be most accurate, even for polyphonic mixtures.

For each challenge task, a development dataset and baseline system will be provided. Challenge evaluation will be done using an evaluation dataset that will be published shortly before the deadline.Task-specific rules are available on the task pages.

The challenges of providing quality respiratory care to persons living in rural or remote communities can be daunting. These populations are often vulnerable in terms of both health status and access to care, highlighting the need for innovation in service delivery. The rapidly expanding options available using telehealthcare technologies have the capacity to allow patients in rural and remote communities to connect with providers at distant sites and to facilitate the provision of diagnostic, monitoring, and therapeutic services. Successful implementation of telehealthcare programs in rural and remote settings is, however, contingent upon accounting for key technical, organizational, social, and legal considerations at the individual, community, and system levels. This review article discusses five types of telehealthcare delivery that can facilitate respiratory care for residents of rural or remote communities: remote monitoring (including wearable and ambient systems; remote consultations (between providers and between patients and providers), remote pulmonary rehabilitation, telepharmacy, and remote sleep monitoring. Current and future challenges related to telehealthcare are discussed. 041b061a72