Topic
multimodal ai
Learned Image Compression Framework SPARC Boosts VLA Robot Control Performance in Bandwidth-Limited Settings
Researchers introduce SPARC (SPatially Adaptive Rate Control), a learned image compression framework tailored for vision-language-action (VLA) models. SPARC adaptively allocates bitrate based on task relevance and uses a tilted rate loss to preserve critical visual patterns. Experiments on robotic benchmarks RoboCasa365, VLABench, and LIBERO show SPARC achieves stronger control performance than conventional codecs at the same bitrate, with real-world benefits for remote robot control.
TimeVista: Researchers Use Vision-Language Models as Judges for Time Series Forecasting Evaluation
Researchers propose using vision-language models (VLMs) as judges for time series forecasting, addressing limitations of traditional point-wise metrics. They introduce TimeVista, a benchmark of 5,563 samples, and show VLMs achieve significantly higher consistency with human preferences than conventional metrics, also assessing Time Series Foundation Models.