NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results

This paper reports on the NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration (BSCVR). The challenge aims to advance research on recovering visually coherent videos from corrupted bitstreams, whose decoding often produces severe spatial-temporal artifacts and content distortion. Built upon recent progress in bitstream-corrupted video recovery, the challenge provides a common benchmark for evaluating restoration methods under realistic corruption settings. We describe the dataset, evaluation protocol, and participating methods, and summarize the final results and main technical trends. The challenge highlights the difficulty of this emerging task and provides useful insights for future research on robust video restoration under practical bitstream corruption.

DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments

The DISPLACE challenge entails a first-of-kind task to perform speaker and language diarization on the same data, as the data contains multi-speaker social conversations in multilingual code-mixed speech. The challenge attempts to benchmark and improve Speaker Diarization (SD) in multilingual settings and Language Diarization (LD) in multi-speaker settings. For this challenge, a natural multilingual, multi-speaker conversational dataset is distributed for development and evaluation purposes. Automatic systems are evaluated on single-channel far-field recordings containing natural code-mix, code-switch, overlap, reverberation, short turns, short pauses, and multiple dialects of the same language. A total of 60 teams from industry and academia have registered for this challenge.