In the fields of health care and clinical monitoring, vision-based Facial Expression Recognition (FER) has achieved significantly progress, but it still faces the challenge of poor generalization ability under unconstrained conditions of occlusions and pose variation. Recently, Vision-Language Model (VLM) has greatly advanced the FER task. However, the existing VLM-based FER methods typically leverage a hard-crafted prompt (e.g., “a photo of [class]”) and only focus on the holistic semantic alignment, which may suffer from modal heterogeneity. In this work, we propose a fine-grained vision-language model via Prompt Masking for FER (PMFER). Specifically, for each expression, we first create fine-grained prompts using facial action units to guide the image encoder to learn discriminative representations. Further, to finely align text prompts and visual action units, we randomly drop a phrase description in the prompts and then predict the dropped phrase by conducting modal cross attention, implicitly promoting fine-grained vision-language alignment. In addition, we also design a modal-adversarial strategy to holistically eliminate the modal difference between visual and textual embeddings in a common latent space. Experimental results demonstrate that our PMFER model outperforms the state-of-the-art methods on several FER benchmarks, especially under the conditions of occlusions and pose variations. Note to Practitioners—Facial expression recognition is very important in health care and clinical monitoring, which provides an useful tool to assess the psychological and physiological conditions of patients. Although FER has made significant progress with the development of deep learning technologies, it still faces problems in the complex environments (e.g., occlusions and pose variations). To address the above issues, we propose a novel FER method in this work based on the recent vision-language model. It takes RGB image and text prompts as input and finally predicts the expression classification. Different from the existing methods, the proposed PMFER can enable fine-grained modal alignment for facial key units. Compared with the state-of-the-art methods on the public datasets, it can achieve better results, especially under the conditions of occlusions and pose variations. Also, we evaluate the proposed method on a real-world pain dataset, and the results demonstrate that PMFER has a good generalization and can be applied to health care.

Human-Object Interaction (HOI) detection, as a foundational task in human-centric understanding, aims to detect interactive triplets in real-world scenarios. To better distinguish diverse HOIs within an open-world context, current HOI detectors utilize pre-trained Visual-Language Models (VLMs) to extract prior knowledge through textual prompts (i.e., descriptive texts for each HOI instance). However, relying on predetermined descriptive texts, such approaches only acquire a fixed set of textual knowledge for HOI prediction, consequently resulting in inferior performance and limited generalization. To remedy this, we propose a novel VLM-based method, which jointly performs prompting learning from both visual and textual perspectives and synergizes visual-textual prompting for HOI detection. Initially, we design a hierarchical adaptation architecture to perform progressive prompting: visual prompting is facilitated through gradual token migration from VLM's image encoder, while textual prompting is initialized with progressively leveled interaction descriptions. In addition, to synergize the visual-textual prompting learning, a text-supervising and image-tuning loop is introduced, in which the text-supervising stage guides visual prompting learning through contrastive learning and the image-tuning stage refines textual prompting by modal matching. Finally, we employ an interaction-aware knowledge merging mechanism to effectively transfer visual-textual knowledge encapsulated within synergistic prompting for HOI detection. Extensive experiments on two benchmarks demonstrate that our proposed method outperforms the state-of-the-art ones, under both supervised and zero-shot settings.

Lifelong person re-identification (LReID) aims to continuously learn from sequential data streams, enabling cross-camera matching of individuals over time. A critical challenge in LReID lies in balancing the preservation of previously acquired knowledge with the incremental acquisition of new information, due to task-level gaps and limited representation capacity. Conventional methods relying on CNN backbones struggle to fully capture the diverse perspectives of each instance, leading to suboptimal model performance. To tackle these limitations, we propose a diverse representation embedding (DRE) framework that balances preserving old knowledge with adapting to new information. Specifically, our DRE incorporates a robust Transformer-based backbone that utilizes maximum embedding (ME) and multiple class tokens to generate overlapping representations for each instance. To further enhance the model's representation capacity, we design an adaptive constraint module (ACM), which performs integration and discrimination operations on overlapping representations to yield diverse yet diverse representations. Furthermore, we propose two strategies: knowledge update (KU) and knowledge preservation (KP), implemented within the adjustment and learner models, respectively. The KU strategy enhances the learner model's ability to adapt to new information by leveraging prior knowledge from the adjustment model. The KP strategy ensures the retention of historical knowledge while maintaining the model's adaptability. Extensive experiments validate that our DRE surpasses state-of-the-art approaches across large-scale, occluded, and holistic datasets, demonstrating significant performance gains. Our code is available at https://github.com/LiuShiBen/DRE.

Recently, Vision-Language Model (VLM) has greatly ad-vanced the Human-Object Interaction (HOI) detection. The existing VLM-based HOI detectors typically adopt a hand-crafted template (e.g., a photo of a person [action] a/an [object]) to acquire text knowledge through the VLM text encoder. However, such approaches, only encoding the action-specific text prompts in vocabulary level, may suffer from learning ambiguity without exploring the fine-grained clues from the perspective of interaction context. In this paper, we propose a novel method to discover Syntactic Interaction Clues for HOI detection (SICHOI) by using VLM. Specifically, we first investigate what are the essen-tial elements for an interaction context, and then establish a syntactic interaction bank from three levels: spatial relationship, action-oriented posture and situational condition. Further, to align visual features with the syntactic interaction bank, we adopt a multi-view extractor to jointly aggre-gate visual features from instance, interaction, and image levels accordingly. In addition, we also introduce a dual cross-attention decoder to perform context propagation be-tween text knowledge and visual features, thereby enhancing the HOI detection. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on HICO-DET and V-COCO.

Universal object detectors aim to detect any object in any scene without human annotation, exhibiting superior generalization. However, the current universal object detectors show degraded performance in harsh weather, and their insufficient real-time capabilities limit their application. In this paper, we present Uni-YOLO, a universal detector designed for complex scenes with real-time performance. Uni-YOLO is a one-stage object detector that uses general object confidence to distinguish between objects and backgrounds, and employs a grid cell regression method for real-time detection. To improve its robustness in harsh weather conditions, the input of Uni-YOLO is adaptively enhanced with a physical model-based enhancement module. During training and inference, Uni-YOLO is guided by the extensive knowledge of the vision-language model CLIP. An object augmentation method is proposed to improve generalization in training by utilizing multiple source datasets with heterogeneous annotations. Furthermore, an online self-enhancement method is proposed to allow Uni-YOLO to further focus on specific objects through self-supervised fine-tuning in a given scene. Extensive experiments on public benchmarks and a UAV deployment are conducted to validate its superiority and practical value.