5 accepted papers
AI-generated image (AIGI) detection is undergoing a critical transition from laboratory benchmarks to open-world adversarial defense. The prevalent paradigm focuses on finding static feature spaces, assuming that some invariant artifacts learned from historical data can achieve universal zero-shot g…
Time-Dependent Differential Equations (TDDEs) model dynamical processes across science and engineering, but time-critical applications require solvers delivering high-fidelity trajectories under stringent latency constraints. Most existing TDDE solvers are limited by time discretization, forcing a l…
Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and full-precision models. To reduce it, existing quantization approaches require…
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed i…
We aim to endow a robot with the ability to learn manipulation concepts that link natural language instructions to motor skills. Our goal is to learn a single multi-task policy that takes as input a natural language instruction and an image of the initial scene and outputs a robot motion trajectory…