9 accepted papers
Zero-shot anomaly detection (ZSAD) aims to identify unseen anomalies without abnormal supervision, which is essential for open-world scenarios. Recent vision-language models such as CLIP enable anomaly reasoning through shared visual-textual embeddings, but existing methods often rely on coarse prom
While model-based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to generalize across structurally similar scenes and remain vulnerable to spurious variations such as textures or color shifts. From a cognitive scienc…
In this letter, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ArenaSim</i>, a novel simulation platform designed for realistic and efficient self-play learning in multi-robot cooperative-competitive games. Compared to previous simulati
3D Gaussian Splatting (3DGS) has been widely discussed due to its impressive ability to provide fast and high-fidelity reconstruction. However, the training efficiency of 3D Gaussian Splatting limits its application in real-time tasks such as Simultaneous Localization and Mapping (SLAM). In this pap…
Learned image compression (LIC) has demonstrated superior rate-distortion (R-D) performance compared to traditional codecs, but is challenged by training inefficiency that could incur more than two weeks to train a state-of-the-art model from scratch. Existing LIC methods overlook the slow convergen…
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years. However, existing LIC methods are redundant in latent representation due to limitations in capturing anisotropic frequency components and preserving directional details. T…
Collaborative perception allows for information sharing between multiple agents such as vehicles and infrastructure to obtain a comprehensive view of the environment through communication and fusion. Current research on multi-agent collaborative perception systems often assumes ideal communication a…
The diffusion model has demonstrated impressive performance in offline reinforcement learning. However, non-deterministic sampling in diffusion models can lead to unstable performance. Furthermore, the lack of confidence measurements makes it difficult to evaluate the reliability and trustworthiness…
Reversible residual network naturally extends the linear lifting scheme with no theoretic guarantee. In this paper, we propose a reversible autoencoder (Rev-AE) with this extended non-linear lifting scheme to improve image reconstruction. Nonlinear prediction and update operators are designed based…