Attribute-Specific Fashion Retrieval (ASFR) focuses on retrieving images based on fine-grained, attribute-specific criteria rather than naive global visual similarity, enabling more precise and interpretable search results. Existing ASFR methods ideally assume that all attribute semantics are in-domain distributions of the training datasets. However, realistic scenarios are generally more complex and naturally contain unseen attribute information, often resulting in ungeneralizable retrieval outcomes. In this paper, we take the first step to address the new and challenging open-world ASFR setting, which involves handling diverse and practical attributes instead of relying solely on predefined attribute sets in closed-world scenarios. Specifically, to comprehend unseen attributes, we propose a novel LLM-based Commonsense Knowledge Infusion (CoKi) framework that integrates commonsense knowledge as complementary context into attribute representations using a Large Language Model (LLM). By infusing such LLM-based commonsense knowledge through descriptive contexts, our method enables robust semantic enrichment and effective generalization to unseen attributes. Additionally, we introduce a modality-switchable prompt and an imputation mechanism to ensure model robustness across diverse input configurations by dynamically adapting to missing modalities. Extensive experiments demonstrate that our approach not only achieves state-of-the-art in-domain retrieval performance but also significantly enhances adaptability to unseen attributes and cross-domain generalization, establishing a new benchmark for fine-grained fashion retrieval in open-world scenarios. Our source code is publicly available at https://github.com/HuiGuanLab/CoKi.

As a widely explored multi-modal task, Temporal Sentence Grounding in videos (TSG) endeavors to retrieve a specific video segment matched with a given query text from a video. The traditional paradigm for TSG generally assumes that relevant segments always exist within a given video. However, this assumption is restrictive and unrealistic in real-world applications where the existence of a query-related segment is uncertain, easily resulting in erroneous grounding. Motivated by the research gap and practical application, this paper introduces a new task, named Temporal Sentence Grounding with Relevance Feedback (TSG-RF) in videos, which accommodates the possibility that a video may or may not include a segment related to the query. This task entails localizing precise video segments that semantically align with the query text when such content is present, while delivering definitive feedback on the non-existence of related segments when absent. Moreover, we propose a novel Relation-aware Temporal Sentence Grounding (RaTSG) network for addressing this challenging task. This network first reformulates the TSG-RF task as a foreground-background detection problem by investigating whether the query-related semantics exist in both frame and video levels. Then, a multi-granularity relevance discriminator is exploited to produce precise video-query relevance feedback and a relation-aware segment grounding module is employed to selectively conduct the grounding process, dynamically adapting to the presence or absence of query-related segments in videos. To validate our RaTSG network, we reconstruct two popular TSG datasets, establishing a rigorous benchmark for TSG-RF. Experimental results demonstrate the effectiveness of our proposed RaTSG for the TSG-RF task. Our source code is available at https://github.com/HuiGuanLab/RaTSG.

High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receiving end. To address this issue, we propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images. The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time. Thus, we introduce a simple content loss to preserve the identity information, and design a minimax optimization to deal with the contradictory aspects. We demonstrate that the robustness results can be transferred across different cover datasets. In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method, providing a multitask framework. The experiments under the multitask scenario show the effectiveness of our framework compared with other visual information hiding methods and state-of-the-art high-capacity image steganography methods. The code is available at https://github.com/jiabaocui/MIAIS.

Photorealistic style transfer is a challenging task, which demands the stylized image remains real. Existing methods are still suffering from unrealistic artifacts and heavy computational cost. In this paper, we propose a novel Style-Corpus Constrained Learning (SCCL) scheme to address these issues. The style-corpus with the style-specific and style-agnostic characteristics simultaneously is proposed to constrain the stylized image with the style consistency among different samples, which improves photorealism of stylization output. By using adversarial distillation learning strategy, a simple fast-to-execute network is trained to substitute previous complex feature transforms models, which reduces the computational cost significantly. Experiments demonstrate that our method produces rich-detailed photorealistic images, with 13 ~ 50 times faster than the state-of-the-art method (WCT2).

In order to reduce fuel consumption, many vehicles are equipped with idle start-stop systems. However, due to complex environment in the real world, vehicles with start-stop systems often experience short-term idling and frequent start-stops. It may greatly accelerate equipment deterioration hence reduce driving comfort. To resolve this problem, we propose SSVisual, an intelligent start-stop system by utilizing collected traffic information. Novel approaches are developed to effectively detect the states of traffic lights and traffic conditions based on image recognition techniques. Given the collected information, SSVisual can determine if it is necessary to shut down the engine at the current idle speed. Moreover, SSVisual can be used for both online and offline environments to visualize the performance of different strategies for research purpose.

With the increasing number of vehicles, the number of traffic accidents also increases, especially at traffic lights. To enhance the driving safety at traffic lights, in this paper, we propose an intelligent safe driving assistant to provide drivers with driving advice based on traffic light phases, which information has been neglected by existing research. The driving assistant consists of an image recognition system with a single on-board camera, which can ameliorate the difficulties of observing traffic light phases. The recognition system obtains traffic light countdown information using a Convolutional Neural Network, and estimates the countdown time using the results of traffic light information. In addition, we develop a model to calculate the distance between the traffic light and vehicle by using the information of camera and traffic light. Based on the traffic light phase and the distance obtained, the driving assistant can provide a velocity control strategy to improve driver's safety. Finally, extensive experiments are conducted to verify the effectiveness of proposed methods.