Autonomous vehicle trajectory planning faces significant challenges in dynamic traffic environments due to the complex and mixed causal relationships between critical scene elements (e.g., pedestrians, vehicles, road markings) and safe decision-making. To identify the causal factors influencing planning outcomes, we propose Causal-Planner, which disentangles the scene interaction graph into causal and confounding components via attention-based adversarial graph learning. Additionally, we introduce a long-short-term episodic memory gating (LSTEM) module that enhances causal interaction disentangling by adaptively capturing evolving causal relationships in dynamic scenarios through bidirectional gated memory fusion. Extensive experiments on the nuPlan dataset suggest that Causal-Planner achieves competitive performance, performing well in both Test-random and Test-hard scenarios under open-loop and closed-loop evaluations. The code will be publicly available at https://github.com/Yyb-XJTU/Causal-Planner.

Image steganography discreetly embeds secret information within a carrier, allowing covert communication and enabling the receiver to extract the concealed data when needed. Previous techniques for image steganography had limitations in achieving imperceptibility and security when dealing with images containing intricate textures. In this paper, we introduce DenseJIN, an innovative model for dense depth image steganography. DenseJIN joins invertible and noninvertible mechanisms to achieve effective and secure information hiding. The invertible component of DenseJIN ensures that the stego image maintains high imperceptibility and security, while the noninvertible component enables high-quality recovery of the secret image. In the invertible component, we employ a dense connection for each invertible block in the forward process and a straightforward series connection during the reverse process. In the forward process of the network, the secret image is embedded, while the backward process is responsible for extracting the embedded secret image. To perform the noninvertible step, we incorporate a modified Unet architecture, enabling deep fine-grained feature extraction from cover images and secret images. Our experimental results indicate that DenseJIN surpasses other contemporary image steganography methods. On average, DenseJIN achieves a remarkable improvement of over 1.75 dB in PSNR for secret image recovery across DIV2K, COCO and ImageNet.

Temporal Sentence Grounding in Video (TSGV) is troubled by dataset bias issue, which is caused by the uneven temporal distribution of the target moments for samples with similar semantic components in input videos or query texts. Existing methods resort to utilizing prior knowledge about bias to artificially break this uneven distribution, which only removes a limited amount of significant language biases. In this work, we propose the bias-conflict sample synthesis and adversarial removal debias strategy (BSSARD), which dynamically generates bias-conflict samples by explicitly leveraging potentially spurious correlations between single-modality features and the temporal position of the target moments. Through adversarial training, its bias generators continuously introduce biases and generate bias-conflict samples to deceive its grounding model. Meanwhile, the grounding model continuously eliminates the introduced biases, which requires it to model multi-modality alignment information. BSSARD will cover most kinds of coupling relationships and disrupt language and visual biases simultaneously. Extensive experiments on Charades-CD and ActivityNet-CD demonstrate the promising debiasing capability of BSSARD. Source codes are available at https://github.com/qzhb/BSSARD.

Compared with cloud computing, edge computing can provide users with computing services that lower response latency and reduce bandwidth pressure, enabling jobs to be processed in the proximity of users. Due to diverse user requirements and limited network resources, it is crucial to guarantee user experience through efficient task scheduling. However, the complexity of network environments and the inherent dependencies between different tasks from each job make it challenging. Given the dynamicity of network conditions, computation resources and service requirements in wireless networks, we have provided a two-level elastic scheduling scheme for dependent task processing. First, a novel scheduling architecture is presented to decouple executor deployment and task assignment, facilitating the increase of resource utilization and the flexibility and elasticity of task scheduling. Then, an optimization problem has been formulated to minimize the execution delay of all jobs with consideration of the characteristics of network networks and task dependencies. After that, an enhanced first come first serve strategy has been employed to make task assignments based on the devised task sorting algorithm. Finally, extensive simulations have been done to evaluate the performance of our proposed algorithm, and the results have shown that our proposed algorithm performs better than benchmark algorithms.

Monocular online map segmentation is of great significance to mapless autonomous driving, and the core step is the View Transformation Module (VTM), which is used to transfer feature from the image perspective to the Bird-Eye-View (BEV). Most existing methods directly draw from the field of 3D object perception, either projecting 2D features into 3D space based on depth estimation, or projecting 3D coordinates into 2D images to query corresponding features, while ignoring the geometry and semantics from the ground surface. In this paper, we proposed a ground aware forward-backward view transformation module. The forward projection is used to generate the initial sparse BEV features and the geometric and semantic prior information of the ground surface. The backward module refines the BEV features based on the geometric and semantic priors, thereby improving the accuracy of map segmentation. In addition, the data partitioning of most previous related works has the problem of data leakage, so we repartitioned and experimented on the nuScense data set to conduct a fair evaluation. Experimental results demonstrate that our method achieves the highest accuracy on the test set. Code will be released at https://github.com/Brickzhuantou/MonoBEVseg.

Temporal sentence grounding in video has witnessed significant advancements, but suffers from substantial dataset bias, which undermines its generalization ability. Existing debias approaches primarily concentrate on well-known distribution and linguistic biases, while overlooking the relationship among different biases, limiting their debias capability. In this work, we delve into the existence of visual bias and combinatorial bias in the widely used datasets, and introduce a collaborative debias structure that can be seamlessly integrated into present methods. It encompasses four low-capacity models, a re-label module, and a main model. Each biased model deliberately leverages bias as shortcut information to accurately perform grounding, achieved by customizing the appropriate model structure and input data format to align with the bias characteristics. During the training phase, the gradient descent direction for optimizing the main model should align with the negative gradient descent direction of the biased model that is optimized by utilizing ground truth labels. Subsequently, the re-label module introduces a gradient aggregation function, consolidating the gradient descent direction from these biased models and constructing new labels to compel the main model to effectively capture multi-modality alignment features instead of relying on shortcut contents for grounding. Finally, we design two debias structures, P-Debias and C-Debias, to exploit the independence and inclusion relationships between different types of biases. Extensive experiments on multiple span-based models over Charades-CD and ActivityNet-CD demonstrate the exceptional debias capability of our strategy (https://github.com/qzhb/CDS).