Haiyan Yu

Hongyu Zhou, Zheng Ge, Songtao Liu et al.

To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this work, we propose replacing the sparse pseudo-boxes with the dense prediction as a united and straightforward form of pseudo-label. Compared to the pseudo-boxes, our Dense Pseudo-Label (DPL) does not involve any post-processing method, thus retaining richer information. We also introduce a region selection technique to highlight the key information while suppressing the noise carried by dense labels. We name our proposed SS-OD algorithm that leverages the DPL as Dense Teacher. On COCO and VOC, Dense Teacher shows superior performance under various settings compared with the pseudo-box-based methods.

Chenxi Lin, Jiayu Ren, Guoxiu He et al.

While large language models (LLMs) excel at understanding and generating plain text, they are not tailored to handle hierarchical text structures or directly predict task-specific properties such as text rating. In fact, selectively and repeatedly grasping the hierarchical structure of large-scale text is pivotal for deciphering its essence. To this end, we propose a novel framework for hierarchical text rating utilizing LLMs, which incorporates Recurrent Alignment with Hard Attention (RAHA). Particularly, hard attention mechanism prompts a frozen LLM to selectively focus on pertinent leaf texts associated with the root text and generate symbolic representations of their relationships. Inspired by the gradual stabilization of the Markov Chain, recurrent alignment strategy involves feeding predicted ratings iteratively back into the prompts of another trainable LLM, aligning it to progressively approximate the desired target. Experimental results demonstrate that RAHA outperforms existing state-of-the-art methods on three hierarchical text rating datasets. Theoretical and empirical analysis confirms RAHA's ability to gradually converge towards the underlying target through multiple inferences. Additional experiments on plain text rating datasets verify the effectiveness of this Markov-like alignment. Our data and code can be available in https://github.com/ECNU-Text-Computing/Markov-LLM.

Yuanhao Huang, Yilong Ren, Jinlei Wang et al.

Autonomous vehicles are typical complex intelligent systems with artificial intelligence at their core. However, perception methods based on deep learning are extremely vulnerable to adversarial samples, resulting in security accidents. How to generate effective adversarial examples in the physical world and evaluate object detection systems is a huge challenge. In this study, we propose a unified joint adversarial training framework for both 2D and 3D domains, which simultaneously optimizes texture maps in 2D image and 3D mesh spaces to better address intra-class diversity and real-world environmental variations. The framework includes a novel realistic enhanced adversarial module, with time-space and relighting mapping pipeline that adjusts illumination consistency between adversarial patches and target garments under varied viewpoints. Building upon this, we develop a realism enhancement mechanism that incorporates non-rigid deformation modeling and texture remapping to ensure alignment with the human body's non-rigid surfaces in 3D scenes. Extensive experiment results in digital and physical environments demonstrate that the adversarial textures generated by our method can effectively mislead the target detection model. Specifically, our method achieves an average attack success rate (ASR) of 70.13% on YOLOv12 in physical scenarios, significantly outperforming existing methods such as T-SEA (21.65%) and AdvTexture (19.70%). Moreover, the proposed method maintains stable ASR across multiple viewpoints and distances, with an average attack success rate exceeding 90% under both frontal and oblique views at a distance of 4 meters. This confirms the method's strong robustness and transferability under multi-angle attacks, varying lighting conditions, and real-world distances. The demo video and code can be obtained at https://github.com/Huangyh98/AdvReal.git.