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This article explains the 1 Billion Row Challenge (1BRC) for Java 21, detailing the data format, required output, how to build and run the benchmark, optimization options, submission rules, and the evaluation environment used to rank participants.
The 2023 Chinese Continuous Visual Speech Recognition Challenge (CNVSRC), organized by Tsinghua University and partners, introduces the large-scale CN-CVS dataset, defines single- and multi-speaker lip‑reading tasks, provides baseline Conformer models, outlines registration, data access, evaluation metrics, and competition schedule.
The iQIYI M2VoC Challenge at ICASSP 2021 invites researchers to tackle low‑resource multi‑speaker, multi‑style voice cloning by providing Mandarin datasets, few‑shot and extremely few‑shot tracks with strict data rules, MOS‑based subjective evaluation, and a $9,600 prize pool for top submissions.
The iCartoonFace Challenge invites participants to develop efficient algorithms for detecting and recognizing cartoon faces using large, meticulously annotated datasets—50,000 images for detection and nearly 390,000 for recognition—while meeting strict model size and latency limits and submitting detailed methods and code.
In the 2019 iQIYI Celebrity Video Identification Challenge, our team secured fifth place by accurately recognizing video identities using mAP scoring, and this article shares the strategies, insights, and experiences of the top‑five teams, emphasizing a straightforward, pragmatic approach championed by iQIYI’s technology product team.
The CVPR 2019 WAD Autonomous Driving Challenge, hosted in Long Beach, introduces four new tasks—including object‑detection and tracking transfer‑learning tracks using Didi’s massive D²‑City and Berkeley’s BDD100K datasets, plus a large‑scale detection interpolation track—aimed at advancing vision algorithms under diverse, difficult driving conditions, with global teams invited to register by May 31 and winners announced at the workshop on June 17.
The AVS Working Group, backed by iQIYI, has launched the Deep Learning Based Video Coding Loop Filter Challenge to spur research on AI‑driven loop filters that can cut bitrate by over 15%, inviting participants to submit Caffe models on the TAVS3 platform with ongoing benchmarking and potential prizes, while encouraging contributions to the upcoming AVS video coding standard.