9 accepted papers
Recent generative models allow robots to generate future visual outcomes for action guidance, yet most still address imagination and control independently, resulting in visually coherent rollouts but physically inconsistent behaviors. While structural priors enhance spatial grounding, these methods
Tiny machine learning (TinyML) has attracted heightened attention for its ability to provide low-cost and instantaneous performance on edge devices. Particularly, the commonly used microcontroller unit (MCU) imposes extreme constraints on peak memory (SRAM) and storage (Flash). Existing TinyML metho…
Object rearrangement, which involves arranging objects step-by-step to achieve tidy states, is critical in robotic applications. Progress in this area is often constrained by issues such as high-cost data collection and physically infeasible trajectory prediction. To address these challenges, we pro
Data-free quantization is known for quantizing a pre-trained deep neural network without access to any training data, which applies to many real-world scenarios in that the training data is unavailable due to security, user privacy, or proprietary concerns. Most of the existing data-free quantizatio…
Latest trajectory prediction models in real-world autonomous driving systems often rely on online High-Definition (HD) maps to understand the road environment.However, online HD maps suffer from perception errors and feature redundancy, which hinder the performance of HD map-based trajectory predict…
The rapid advancement of large language models (LLMs) has paved the way for their use in solving real-world problems, which in turn has significantly driven the development of tool-assisted LLMs. This progress necessitates thorough evaluation methods. However, existing benchmarks typically only prov…
Large Language Models (LLMs) have demonstrated unparalleled efficacy in natural language processing. However, their high computational demands and memory overheads hinder their broad deployment. To address this, two quantization strategies emerge, including Quantization-Aware Training (QAT) and Post…
This paper proposes a novel, lightweight deep convolutional neural network specifically designed for iris segmentation of noisy images acquired by mobile devices. Unlike previous studies, which only focused on improving the accuracy of segmentation mask using the popular CNN technology, our method i…
Keypoint-based detectors have achieved pretty-well performance. However, incorrect keypoint matching is still widespread and greatly affects the performance of the detector. In this paper, we propose CentripetalNet which uses centripetal shift to pair corner keypoints from the same instance. Centrip…