AutoVisual Fusion Suite: A Comprehensive Evaluation of Image Segmentation and Voice Conversion Tools on HuggingFace Platform

TL;DR

The paper addresses the challenge of assembling reliable image segmentation and voice conversion tools from HuggingFace into a unified AutoVisual Fusion Suite. It systematically evaluates top HF options for image segmentation (notably DETR with ResNet-50 and SegFormer on ADE20K) and for voice conversion (So-Vits-SVC-Fork, RVC-based WebUI, AutoVC, YourTTS), and demonstrates Linux-based deployment with Docker for reproducibility. A central contribution is the integration of frame-wise video segmentation (via SAM and DETR) with voice conversion pipelines to enable joint video editing workflows, aided by practical guidance on GPU-accelerated deployment and open-source code availability. The work highlights the strengths and limitations of current models (e.g., SAM’s label-agnostic masks vs DETR’s object-centric outputs) and points to future improvements in temporal consistency, labeling, and cross-domain compatibility, thereby supporting researchers and engineers in deploying multimodal AI solutions. Overall, AutoVisual Fusion Suite serves as a practical blueprint for building end-to-end multimedia AI pipelines from HF tools, with clear pathways for extension and optimization in real-world Linux environments.

Abstract

This study presents a comprehensive evaluation of tools available on the HuggingFace platform for two pivotal applications in artificial intelligence: image segmentation and voice conversion. The primary objective was to identify the top three tools within each category and subsequently install and configure these tools on Linux systems. We leveraged the power of pre-trained segmentation models such as SAM and DETR Model with ResNet-50 backbone for image segmentation, and the so-vits-svc-fork model for voice conversion. This paper delves into the methodologies and challenges encountered during the implementation process, and showcases the successful combination of video segmentation and voice conversion in a unified project named AutoVisual Fusion Suite.

Figures (21)

• Figure 1: Using detr-resnet-50-panoptic to extract the target and remove the background. We then added the picture with transparent background to the second input image.
• Figure 2: The same thing as figure \ref{['fig:pic1']} one but this time applying the detr-resnet-50-panoptic model to extract multi-task from the target input image.
• Figure 3: Applying image segmentation to video frames.
• Figure 4: Comparison with the state-of-the-art methods UPSNet and Panoptic FPN on the COCO val dataset they retrained PanopticFPN with the same data-augmentation as DETR, on a $18x$ schedule for fair comparison. UPSNet uses the $1x$ schedule, UPSNet-M is the version with multiscale test-time augmentations end.
• Figure 5: Analysis of the number of instances of various classes missed by DETR de-pending on how many are present in the image. We report the mean and the standard deviation. As the number of instances gets close to 100, DETR starts saturating and misses more and more objects end.