4 accepted papers
Choosing appropriate fabrics is critical for meeting functional and quality demands in robotic textile manufacturing, apparel production, and smart retail. We propose MLLM-Fabric, a robotic framework leveraging multimodal large language models (MLLMs) for fabric sorting and selection. Built on a mul…
Choosing appropriate fabrics is critical for meeting functional and quality demands in robotic textile manufacturing, apparel production, and smart retail. We propose MLLM-Fabric, a robotic framework leveraging multimodal large language models (MLLMs) for fabric sorting and selection. Built on a mul
Scale variation is one of the key challenges in object detection. One solution is Image Pyramid, which employs images of multiple resolutions for training. Another solution is Feature Pyramid, which uses multi-scale features for prediction and is widely used in current object detectors due to its hi…
Current state-of-the-art object detectors generally use pre-trained classification networks to extract features, and then utilize feature pyramids to detect objects of different scales. However, classification networks prefer translation invariance and ignore the location information, so directly us…