Learned Image Compression (LIC) has achieved superior performance in recent years, of which the context entropy model is an important component. However, in the context entropy model, there is no deterministic correlation between neighboring channels, and it is difficult to capture inter-channel correlation as well as spatial correlation for further improving the performance. To address this issue, a Cubic-Checkerboard conTeXt entropy model (C-CTX) for LIC is proposed in this work, which is able to refer uniformly across the channel domain and maintain the correlations in the spatial domain. To make neighboring channels have more similar distribution, Cubic Checkerboard Mask (CCM) with channel- wise mask convolution is utilized to achieve uniform distribution in different domains and Channel Wise Re-Arrangement (CWRA) is performed in terms of entropy. Based on CCM and CWRA, two Feature Disentangle Modules (FDMs) are designed in C-CTX to project the context information within sub-spaces for catching spatial correlation and channel correlation separately. Extensive experimental evaluations show that our method outperforms the state-of-the-art works on six datasets, i.e., Kodak, Tecnick, CLIC'20, CLIC'21, CLIC'22, and JPEG-AI.

A critical use case of SLAM for mobile robots is to support localization during task-directed navigation. Current SLAM benchmarks overlook the importance of repeatability (precision) despite its impact on real-world deployments. TaskSLAM-Bench, a task-driven approach to SLAM benchmarking, addresses this gap. It employs precision as a key metric, accounts for SLAM’s mapping capabilities, and has easy-to-meet requirements. Simulated and real-world evaluation of SLAM methods provide insights into the navigation performance of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM precision may match that of 2D LiDAR SLAM in indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots.

Safe quadrupedal navigation through unknown environments is a challenging problem. This paper proposes a hierarchical vision-based planning framework (GPF-BG) integrating our previous Global Path Follower (GPF) navigation system and a gap-based local planner using Bézier curves, so called$B$ézier Gap (BG). This BG-based trajectory synthesis can generate smooth trajectories and guarantee safety for point-mass robots. With a gap analysis extension based on non-point, rectangular geometry, safety is guaranteed for an idealized quadrupedal motion model and significantly improved for an actual quadrupedal robot model. Stabilized perception space improves performance under oscillatory internal body motions that impact sensing. Simulation-based and real experiments under different benchmarking configurations test safe navigation performance. GPF-BG has the best safety outcomes across all experiments.

This paper explores the use of passive, stereo sensing for vision-based navigation. The traditional approach uses dense depth algorithms, which can be computationally costly or potentially inaccurate. These drawbacks compound when including the additional computational demands associated to the sensor fusion, collision checking, and path planning modules that interpret the dense depth measurements. These problems can be avoided through the use of the stixel representation, a compact and sparse visual representation for local free-space. When integrated into a Planning in Perception Space based hierarchical navigation framework, stixels permit fast and scalable navigation for different robot geometries. Computational studies quantify the processing performance and demonstrate the favorable scaling properties over comparable dense depth methods. Navigation benchmarking demonstrates more consistent performance across high and low performance compute hardware for PiPS-based stixel navigation versus traditional hierarchical navigation.

In this paper we propose an attack method to embed digital watermarking invisibly into a clean example to generate an adversarial example to interfere with the classification of deep learning models. Specifically, we propose an optimization algorithm called Non-Dominated Sorting Genetic Algorithm with Particle Swarm Optimization (NSGA-PSO) to generate adversarial digital watermarking in the black-box attack mode with a few queries from the models to be attacked. Extensive experiments on ImageNet and CIFAR-10 datasets demonstrate that our method can efficiently generate adversarial examples with higher attack success rates than existing black-box attack methods. Furthermore, showing satisfactory transferability across different network models and greater robustness against image transformation defense methods.