Recently, the visual programming framework (VisProg) has emerged as a significant framework for executing compositional visual tasks due to its interpretability and flexibility. However, the performance of VisProg on specific Visual Reasoning (VR) tasks is markedly inferior compared to well-trained task-specific models since its employed visual sub-modules have limited generalization capabilities. Due to the non-differentiability of VisProg, it is quite challenging to improve these visual sub-modules within VisProg for the specific VR task while maintaining their generalizability on the un-seen tasks. Attempt to overcome these difficulties, we propose CLVP, a Continuous Learning paradigm for VisProg across various visual reasoning tasks. Specifically, our CLVP distills the capabilities of well-trained task-specific models into the visual sub-modules in a stepwise and anti-forgetting manner. This can continually improve the performance of VisProg on multiple visual tasks while preserving the flexibility of VisProg. Extensive and comprehensive experimental results demonstrate that our CLVP obtains significant performance gains on specific VR benchmarks, i.e., GQA (+1.4%) and NLVRv2 (+5.6%), compared to the VisProg baseline, and also maintains a promising generalizability for VR on un-seen and previous learned tasks.

In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts.