Face retouching aims to remove facial imperfections from image and videos while at the same time preserving face attributes. The existing methods are designed to perform non-interactive end-to-end retouching, while the ability to interact with users is highly demanded in downstream applications. In this paper, we propose RetouchGPT, a novel framework that leverages Large Language Models (LLMs) to guide the interactive retouching process. Towards this end, we design an instruction-driven imperfection prediction module to accurately identify imperfections by integrating textual and visual features. To learn imperfection prompts, we further incorporate a LLM-based embedding module to fuse multi-modal conditioning information. The prompt-based feature modification is performed in each transformer block, such that the imperfection features are suppressed and replaced with the features of normal skin progressively. Extensive experiments have been performed to verify effectiveness of our design elements and demonstrate that RetouchGPT is a useful tool for interactive face retouching and achieves superior performance over state-of-the-arts.

Knowledge Graph Question Answering (KGQA) addresses natural language questions by leveraging structured information stored in knowledge graphs. However, existing KGQA methods are overly concerned with improving the quality of responses by retrieving information, neglecting to identify which type of knowledge is truly useful to optimize the performance of the KGQA system, resulting in redundant retrieval. At the same time, these methods have limitations in aligning user intent and insufficient semantic richness in responses. In this work, we propose IP-KGQA, which introduce a intent-aware prompt learning scheme for KGQA framework. A Selection-Driven Efficient Retrieval (SER) module is incorporated in the framework, which classifies user questions to ensure that only long-tail questions are directed to the knowledge graph retrieval to enhance system efficiency. To filter and select the most relevant triplets, aligning retrieved information more closely with user intent, we introduce the User Intent-aware Filtering (UIF) module, where Monte Carlo sampling is applied to obtain the optimal triplets. The Domain-specific Context Prompt Extension (DCPE) module is utilized in collaboration with a fine-tuned large language model (LLM) to integrate domain-specific knowledge into the responses, ensuring that the answers are enriched in terms of semantic quality. Extensive experiments have been conducted on the CommonSenseQA and TriviaQA datasets, which demonstrate that IP-KGQA outperforms the existing methods in terms of retrieval efficiency, answer accuracy and user intent alignment.

As a specific image-to-image translation task, Chinese character style transfer aims to synthesize new character images with the style of reference characters and the content of target ones. This task is still challenging due to the significant number of characters with complex structures. We present an Adversarial Style Interpolation-based approach for reliable Style Transfer (ASIST). To capture style information from reference images, we adopt a style predictor to compete with a style encoder and a generator by predicting the interpolation coefficients of the character images synthesized from the interpolated style features. Further, we incorporate a stroke-aware content predictor, which applies attention on local strokes regardless of the variance in style. By jointly optimized with the content predictor, the generator is able to attend on the structural integrity of synthesized characters. Extensive experiments are performed to verify that the proposed approach can synthesize high-quality stylized Chinese characters over the leading methods.

Human action recognition (HAR) is one of most important tasks in video analysis. Since video clips distributed on networks are usually untrimmed, it is required to accurately segment a given untrimmed video into a set of action segments for HAR. As an unsupervised temporal segmentation technology, subspace clustering learns the codes from each video to construct an affinity graph, and then cuts the affinity graph to cluster the video into a set of action segments. However, most of the existing subspace clustering schemes not only ignore the sequential information of frames in code learning, but also the negative effects of noises when cutting the affinity graph, which lead to inferior performance. To address these issues, we propose a sequential order-aware coding-based robust subspace clustering (SOAC-RSC) scheme for HAR. By feeding the motion features of video frames into multi-layer neural networks, two expressive code matrices are learned in a sequential order-aware manner from unconstrained and constrained videos, respectively, to construct the corresponding affinity graphs. Then, with the consideration of the existence of noise effects, a simple yet robust cutting algorithm is proposed to cut the constructed affinity graphs to accurately obtain the action segments for HAR. The extensive experiments demonstrate the proposed SOAC-RSC scheme achieves the state-of-the-art performance on the datasets of Keck Gesture and Weizmann, and provides competitive performance on the other 6 public datasets such as UCF101 and URADL for HAR task, compared to the recent related approaches.

In the Vehicular Ad hoc Networks (VANET) environment, recognizing traffic accident events in the driving videos captured by vehicle-mounted cameras is an essential task. Generally, traffic accidents have a short duration in driving videos, and the backgrounds of driving videos are dynamic and complex. These make traffic accident detection quite challenging. To effectively and efficiently detect accidents from the driving videos, we propose an accident detection approach based on spatio–temporal feature encoding with a multilayer neural network. Specifically, the multilayer neural network is used to encode the temporal features of video for clustering the video frames. From the obtained frame clusters, we detect the border frames as the potential accident frames. Then, we capture and encode the spatial relationships of the objects detected from these potential accident frames to confirm whether these frames are accident frames. The extensive experiments demonstrate that the proposed approach achieves promising detection accuracy and efficiency for traffic accident detection, and meets the real-time detection requirement in the VANET environment.