Recent advances in embodied intelligence have opened new directions for autonomous agents to operate in complex physical environments. In this work, we propose a Posture-Aware Locomotion Agent (PALA) trained via deep reinforcement learning. Unlike traditional quadruped agents that focus solely on velocity tracking, our agent learns to track task-oriented 6D motion commands, including linear and angular velocities, as well as desired body posture (height, pitch, and roll), in real time using only proprioceptive sensing and external commands. To improve robustness and terrain adaptability, we introduce two key heuristic designs: a progressive reward curriculum and an orientation command resampling strategy. Combined with asymmetric actor-critic training, adversarial motion priors, and domain randomization, these components enable a single policy to generalize zero-shot across diverse and challenging environments. Additionally, we extend PALA to handle high-level instructions by integrating it with an autonomous agent powered by a large language model (LLM), enabling natural language task descriptions to be directly translated into executable 6D commands. Extensive simulation and real-world experiments demonstrate the responsiveness and accuracy of our posture-aware locomotion agent, underscoring its potential as a core component for embodied systems operating in unstructured settings.

Current low-light face detection usually enhances images first and then detects faces. Image enhancement focuses on the global enhancement of the whole image and is suitable for the human perspective. However, face detection requires high quality of local face regions and has different optimization objectives from image enhancement task. Such mismatches between the two tasks result in inferior performance of low-light face detection. To solve these obstacles, we propose end-to-end low-light face detection through global and local enhancement. Specifically, the proposed method consists of two components, i.e., image enhancement and face detection. For image enhancement, both global and local enhancement are explored. Global enhancement utilizes illumination constraints to enhance the overall image illumination, while local enhancement employs a generative adversarial network to improve the illumination of face regions during training. Through a combination of global and local enhancement, we reduce the gap between image enhancement and face detection and optimize the two tasks jointly. Furthermore, regions of the human body may help face detection, especially when face regions are small and blurred. Therefore, we regard body region as context to assist face detection. We use the context assistance module and optimize the annotation of the body regions with the aid of human structure prior knowledge. Experiments on the Dark Face dataset demonstrate the effectiveness of the proposed method.

In this paper, we rethink our earlier work on self-attention based crack segmentation, and propose an upgraded CrackFormer network (CrackFormer-II) for pavement crack segmentation, instead of only for fine-grained crack-detection tasks. This work embeds novel Transformer encoder modules into a SegNet-like encoder-decoder structure, where the basic module is composed of novel Transformer encoder blocks with effective relative positional embedding and long range interactions to extract efficient contextual information from feature-channels. Further, fusion modules of scaling-attention are proposed to integrate the results of each respective encoder and decoder block to highlight semantic features and suppress non-semantic ones. Moreover, we update the Transformer encoder blocks enhanced by the local feed-forward layer and skip-connections, and optimize the channel configurations to compress the model parameters. Compared with the original CrackFormer, the CrackFormer-II is trained and evaluated on more general crack datasets. It achieves higher accuracy than the original CrackFormer, and the state-of-the-art (SOTA) method with$6.7 \times $fewer FLOPs and$6.2 \times $fewer parameters, and its practical inference speed is comparable to most classical CNN models. The experimental results show that it achieves the F-measures on Optimal Dataset Scale (ODS) of 0.912, 0.908, 0.914 and 0.869, respectively, on the four benchmarks. Codes are available athttps://github.com/LouisNUST/CrackFormer-II.

Low illumination face recognition is challenging as details are lacking due to lighting conditions. Retinex theory points out that images can be divided into reflectance with color constancy and ambient illumination. Inspired by this, we propose a knowledge-guided representation disentanglement method to disentangle facial images into face-related and illumination-related features, and then leverage the disentangled face-related features for face recognition. Specifically, the proposed method consists of two components: feature disentanglement and face classifier. Following Retinex, high-dimensional face-related features and ambient illumination-related features are extracted from facial images. Reconstruction and crossreconstruction methods are used to make sure the integrity and accuracy of the disentangled features. Furthermore, we find that the influence of illumination changes on illumination-related features should be invariant for faces of different identities, so we design an illumination offset loss to satisfy the prior invariance for better disentanglement. Finally high-dimensional face-related features are mapped to low-dimensional features through the face classifier for use in face recognition task. Experimental results on low illumination and NIR-VIS datasets demonstrate the superiority and effectiveness of our proposed method.

Cracks are irregular line structures that are of interest in many computer vision applications. Crack detection (e.g., from pavement images) is a challenging task due to intensity in-homogeneity, topology complexity, low contrast and noisy background. The overall crack detection accuracy can be significantly affected by the detection performance on fine-grained cracks. In this work, we propose a Crack Transformer network (CrackFormer) for fine-grained crack detection. The CrackFormer is composed of novel attention modules in a SegNet-like encoder-decoder architecture. Specifically, it consists of novel self-attention modules with 1x1 convolutional kernels for efficient contextual information extraction across feature-channels, and efficient positional embedding to capture large receptive field contextual information for long range interactions. It also introduces new scaling-attention modules to combine outputs from the corresponding encoder and decoder blocks to suppress non-semantic features and sharpen semantic ones. The CrackFormer is trained and evaluated on three classical crack datasets. The experimental results show that the CrackFormer achieves the Optimal Dataset Scale (ODS) values of 0.871, 0.877 and 0.881, respectively, on the three datasets and outperforms the state-of-the-art methods.