Pre-trained Vision-Language Models (VLMs) such as CLIP have shown excellent generalization abilities. However, adapting these large-scale models to downstream tasks while preserving their generalization capabilities remains challenging. Although prompt learning methods have shown promise, they suffer from two fundamental bottlenecks that limit generalization: (a) modality isolation, and (b) hierarchical semantic decay. To address these limitations, we propose HiCroPL, a Hierarchical Cross-modal Prompt Learning framework that establishes bidirectional knowledge flow between text and vision modalities, enabling them to refine their semantics mutually. HiCroPL routes knowledge flows by leveraging the complementary strengths of text and vision. In early layers, text prompts inject relatively clear semantics into visual prompts through a hierarchical knowledge mapper, enhancing the representation of low-level visual semantics. In later layers, visual prompts encoding specific task-relevant objects flow back to refine text prompts, enabling deeper alignment. Crucially, our hierarchical knowledge mapper allows representations at multi-scales to be fused, ensuring that deeper representations retain transferable shallow semantics thereby enhancing generalization. We further introduce a lightweight layer-specific knowledge proxy to enable efficient cross-modal interactions. Extensive evaluations across four tasks demonstrate HiCroPL's superior performance, achieving state-of-the-art results on 11 benchmarks with significant improvements. Code is available at: https://github.com/zzeoZheng/HiCroPL.

Person re-identification (ReID) is vital for surveillance, tracking, and criminal investigations, yet occlusions often lead to partial information loss and noisy features that significantly degrade ReID performance. Recent CLIP-based occluded person ReID methods have demonstrated promising performance by leveraging cross-modal alignment, but still face two limitations: first, generic text prompts fail to capture the fine-grained semantics of specific samples; second, there is a lack of effective enhancement mechanisms for hard local features in occlusion scenarios. To overcome these limitations, we propose a Semantically Guided and Focused Network (SGFNet), which comprises three synergistic modules. First, to tackle the absence of fine-grained textual descriptions, we design a Segmentation and Text Generation (STG) module that segments pedestrian regions and generates sample-specific text features, providing detailed text descriptions and spatial information for local pedestrian regions. In addition, in order to accurately extract fine-grained features, we propose a Dual-guided Feature Refinement (DGFR) module. This module leverages a spatial attention mechanism guided by dual-semantic information to enhance discriminative fine-grained features while effectively suppressing interference from irrelevant regions. Finally, building upon the DGFR module, we further propose a Hardness-aware Semantic Focus (HASF) module. This module leverages segmentation cues to assess the difficulty of distinguishing local regions and employs a carefully designed Semantic-driven Focal Triplet loss to specifically enhance hard local feature learning, thereby improving the model’s robustness in feature extraction under occlusion scenarios. Extensive experiments demonstrate the superiority of SGFNet, achieving state-of-the-art performance on three occluded person ReID datasets while maintaining competitive results on three holistic person ReID datasets.

Aviation accidents are frequently related to pilots’ operations, especially during a landing phase. Therefore, accurately evaluating a pilot’s performance during this phase is crucial for minimizing landing risks. Traditional assessment methods, however, primarily focus on discrete monitoring points, failing to capture the continuous and dynamic nature of a pilot’s performance throughout the entire landing phase. To address this issue, we propose a Flight Process Importance (FPI) assessment framework that precisely determines accurate landing timing and captures the diverse operational characteristics of pilots. It consists of two components: Time-varying Importance Coefficient (TIC) and Pilot Characteristics Matrix (PCM). TIC develops a Spatio-Temporally Consistent Attention Network (STCAN) to classify Quick Access Recorder (QAR) data for anomalous event detection. It then determines the importance of different periods during the landing process by analyzing the STCAN model’s response to the data in an interpretable manner. PCM generates a parameter matrix for each flight by deriving the ideal intervals of various parameters through the interquartile range. This matrix is used to identify the duration and intensity of anomalies in operations across different pilots. By integrating TIC and PCM, our framework computes an evaluation matrix for each flight, quantifying the operational risk factors associated with pilots. Experimental results indicate that STCAN significantly surpasses other algorithms on QAR data. FPI provides a more precise and comprehensive assessment of a pilot’s performance. In particular, our findings highlight that the 10 seconds before landing to the touchdown are the most critical period of airplane landing.

Existing studies on knowledge distillation typically focus on teacher-centered methods, in which the teacher network is trained according to its own standards before transferring the learned knowledge to a student one. However, due to differences in network structure between the teacher and the student, the knowledge learned by the former may not be desired by the latter. Inspired by human educational wisdom, this paper proposes a Student-Centered Distillation (SCD) method that enables the teacher network to adjust its knowledge transfer according to the student network's needs. We implemented SCD based on various human educational wisdom, e.g., the teacher network identified and learned the knowledge desired by the student network on the validation set, and then transferred it to the latter through the training set. To address the problems of current deficiency knowledge, hard sample learning and knowledge forgetting faced by a student network in the learning process, we introduce and improve Proportional-Integral-Derivative (PID) algorithms from automation fields to make them effective in identifying the current knowledge required by the student network. Furthermore, we propose a curriculum learning-based fuzzy strategy and apply it to the proposed PID control algorithm, such that the student network in SCD can actively pay attention to the learning of challenging samples after with certain knowledge. The overall performance of SCD is verified in multiple tasks by comparing it with state-of-the-art ones. Experimental results show that our student-centered distillation method outperforms existing teacher-centered ones.

Convolutional neural networks (CNNs) have demonstrated excellent capability in various visual recognition tasks but impose an excessive computational burden. The latter problem is commonly solved by utilizing lightweight sparse networks. However, such networks have a limited receptive field in a few layers, and the majority of these networks face a severe information barrage due to their sparse structures. Spurred by these deficiencies, this work proposes a Squeeze Convolution block with Information Flow Enhancement (SCIFE), comprising a Divide-and-Squeeze Convolution and an Information Flow Enhancement scheme. The former module constructs a multi-layer structure through multiple squeeze operations to increase the receptive field and reduce computation. The latter replaces the affine transformation with the point convolution and dynamically adjusts the activation function’s threshold, enhancing information flow in both channels and layers. Moreover, we reveal that the original affine transformation may harm the network’s generalization capability. To overcome this issue, we utilize a point convolution with a zero initial mean. SCIFE can serve as a plug-and-play replacement for vanilla convolution blocks in mainstream CNNs, while extensive experimental results demonstrate that CNNs equipped with SCIFE compress benchmark structures without sacrificing performance, outperforming their competitors.

Knowledge distillation is a deep learning method that mimics the way that humans teach, i.e., a teacher network is used to guide the training of a student one. Knowledge distillation can generate an efficient student network to facilitate deployment in resource-constrained edge computing devices. Existing studies have typically mined knowledge from a teacher network and transferred it to a student one. The latter can only passively receive knowledge but cannot understand how the former acquires the knowledge, thus limiting the latter’s performance improvement. Inspired by the old Chinese saying “Give a man a fish and you feed him for a day; teach a man how to fish and you feed him for a lifetime,” this work proposes a Skill-transferring Knowledge Distillation (SKD) method to boost a student network’s ability to create new valuable knowledge. SKD consists of two main meta-learning networks: Teacher Behavior Teaching and Teacher Experience Teaching. The former captures the process of a teacher network’s learning behavior in the hidden layers and can predict the teacher network’s subsequent behavior based on previous ones. The latter models the optimal empirical knowledge of a teacher network’s output layer at each learning stage. With their help, a teacher network can provide its actions to a student one in the subsequent behavior and its optimal empirical knowledge in the current stage. SKD’s performance is verified through its application to multiple object recognition tasks and comparison with the state of the art.

Intelligent video surveillance is an important computer vision application in natural environments. Since detected objects under surveillance are usually low-resolution and noisy, their accurate recognition represents a huge challenge. Knowledge distillation is an effective method to deal with it, but existing related work usually focuses on reducing the channel count of a student network, not feature map size. As a result, they cannot transfer "privilege information" hidden in feature maps of a wide and deep teacher network into a thin and shallow student one, leading to the latter's poor performance. To address this issue, we propose a Feature Map Distillation (FMD) framework under which the feature map size of teacher and student networks is different. FMD consists of two main components: Feature Decoder Distillation (FDD) and Feature Map Consistency-enforcement (FMC). FDD reconstructs the shallow texture features of a thin student network to approximate the corresponding samples in a teacher network, which allows the high-resolution ones to directly guide the learning of the shallow features of the student network. FMC makes the size and direction of each deep feature map consistent between student and teacher networks, which constrains each pair of feature maps to produce the same feature distribution. FDD and FMC allow a thin student network to learn rich "privilege information" in feature maps of a wide teacher network. The overall performance of FMD is verified in multiple recognition tasks by comparing it with state-of-the-art knowledge distillation methods on low-resolution and noisy objects.