Dengfeng Chen

Shipeng Liu, Liang Zhao, Dengfeng Chen et al.

Recent crack segmentation methods often follow generic semantic segmentation designs, using stronger backbones, hybrid CNN-Transformer-Mamba encoders, and auxiliary enhancement branches. Although effective, this raises whether stronger generic feature mixing is the most suitable direction for crack segmentation. We instead formulate crack segmentation as sparse structural recovery. Cracks have limited category-level semantics but strong morphological regularities, being thin, sparse, anisotropic, locally fragmented, and easily confused with textures or shadows. Thus, the key bottleneck lies in preserving weak structural evidence, recovering directional continuity, and suppressing background coupling. We propose RIFT, a compact family of morphology-aligned crack segmentation models. Rather than compressing a complex generic architecture, RIFT is simple by design, preserving local evidence, aggregating cooperative directional continuity, and restoring crack structures through lightweight multi-scale fusion. Experiments on four public benchmarks show that RIFT achieves the best or tied-best results across the 16 main metrics against reproduced representative baselines. RIFT-B gives the strongest overall accuracy, while RIFT-T provides the best deployment efficiency with only 0.47M parameters and high inference speed. Topology-aware evaluation, ablations, transfer experiments, and visualizations further verify that task-aligned simplicity can match or surpass complex hybrid architectures when its inductive bias fits crack morphology. Code: https://github.com/xauat-liushipeng/RIFT

Shipeng Liu, Liang Zhao, Dengfeng Chen et al.

Quantifying and evaluating image complexity can be instrumental in enhancing the performance of various computer vision tasks. Supervised learning can effectively learn image complexity features from well-annotated datasets. However, creating such datasets requires expensive manual annotation costs. The models may learn human subjective biases from it. In this work, we introduce the MoCo v2 framework. We utilize contrastive learning to represent image complexity, named CLIC (Contrastive Learning for Image Complexity). We find that there are complexity differences between different local regions of an image, and propose Random Crop and Mix (RCM), which can produce positive samples consisting of multi-scale local crops. RCM can also expand the train set and increase data diversity without introducing additional data. We conduct extensive experiments with CLIC, comparing it with both unsupervised and supervised methods. The results demonstrate that the performance of CLIC is comparable to that of state-of-the-art supervised methods. In addition, we establish the pipelines that can apply CLIC to computer vision tasks to effectively improve their performance.

Shipeng Liu, Zhonglin Zhang, Dengfeng Chen et al.

Accurately assessing image complexity (IC) is critical for computer vision, yet most existing methods rely solely on visual features and often neglect high-level semantic information, limiting their accuracy and generalization. We introduce vision-text fusion for IC modeling. This approach integrates visual and textual semantic features, increasing representational diversity. It also reduces the complexity of the hypothesis space, which enhances both accuracy and generalization in complexity assessment. We propose the D2S (Describe-to-Score) framework, which generates image captions with a pre-trained vision-language model. We propose the feature alignment and entropy distribution alignment mechanisms, D2S guides semantic information to inform complexity assessment while bridging the gap between vision and text modalities. D2S utilizes multi-modal information during training but requires only the vision branch during inference, thereby avoiding multi-modal computational overhead and enabling efficient assessment. Experimental results demonstrate that D2S outperforms existing methods on the IC9600 dataset and maintains competitiveness on no-reference image quality assessment (NR-IQA) benchmark, validating the effectiveness and efficiency of multi-modal fusion in complexity-related tasks. Code is available at: https://github.com/xauat-liushipeng/D2S

Shipeng Liu, Liang Zhao, Dengfeng Chen et al.

Tunnel inspection requires outputs that can support defect localization, measurement, severity grading, and engineering documentation. Existing training-free foundation-model pipelines usually stop at coarse open-vocabulary proposals, which are difficult to use directly in interference-heavy tunnel scenes. We propose a training-free framework TunnelMIND. Specifically, language-guided defect proposals are not treated as final outputs; instead, their spatial support is recalibrated at inference time through dense visual consistency, so that coarse semantic anchors can be transformed into more reliable prompts under tunnel-specific hard negatives. The resulting masks are further reconstructed into structured defect entities with category, location, geometry, severity, and context attributes, which are then mapped to retrieval-grounded explanation and engineering-readable report generation under expert knowledge constraints. On visible, GPR, and road defect tasks, TunnelMIND achieves F1 scores of 0.68, 0.78, and 0.72, respectively. Overall, TunnelMIND shows that training-free tunnel inspection can move beyond coarse localization toward structured defect evidence for engineering assessment.

Shipeng Liu, Liang Zhao, Dengfeng Chen

As a fundamental visual attribute, image complexity significantly influences both human perception and the performance of computer vision models. However, accurately assessing and quantifying image complexity remains a challenging task. (1) Traditional metrics such as information entropy and compression ratio often yield coarse and unreliable estimates. (2) Data-driven methods require expensive manual annotations and are inevitably affected by human subjective biases. To address these issues, we propose CLIC, an unsupervised framework based on Contrastive Learning for learning Image Complexity representations. CLIC learns complexity-aware features from unlabeled data, thereby eliminating the need for costly labeling. Specifically, we design a novel positive and negative sample selection strategy to enhance the discrimination of complexity features. Additionally, we introduce a complexity-aware loss function guided by image priors to further constrain the learning process. Extensive experiments validate the effectiveness of CLIC in capturing image complexity. When fine-tuned with a small number of labeled samples from IC9600, CLIC achieves performance competitive with supervised methods. Moreover, applying CLIC to downstream tasks consistently improves performance. Notably, both the pretraining and application processes of CLIC are free from subjective bias.

Shipeng Liu, Liang Zhao, Dengfeng Chen

Unsupervised image complexity representation often suffers from bias in positive sample selection and sensitivity to image content. We propose CLICv2, a contrastive learning framework that enforces content invariance for complexity representation. Unlike CLIC, which generates positive samples via cropping-introducing positive pairs bias-our shifted patchify method applies randomized directional shifts to image patches before contrastive learning. Patches at corresponding positions serve as positive pairs, ensuring content-invariant learning. Additionally, we propose patch-wise contrastive loss, which enhances local complexity representation while mitigating content interference. In order to further suppress the interference of image content, we introduce Masked Image Modeling as an auxiliary task, but we set its modeling objective as the entropy of masked patches, which recovers the entropy of the overall image by using the information of the unmasked patches, and then obtains the global complexity perception ability. Extensive experiments on IC9600 demonstrate that CLICv2 significantly outperforms existing unsupervised methods in PCC and SRCC, achieving content-invariant complexity representation without introducing positive pairs bias.