Post-hoc and inherently interpretable methods have shown great success in uncovering the inner workings of black-box models, whether by examining them after training or by explicitly designing for interpretability. While these approaches effectively narrow the semantic gap between a model's latent space and human understanding, they typically extract only high-level semantics from the model's final feature map. As a result, they provide a limited perspective on the decision-making process. We argue that explanations lacking insight into both lower- and mid-level semantics cannot be considered fully faithful or genuinely useful. To address this issue, we introduce the Multi-Level Concept Prototypes Classifier (MCPNet), which offers a more holistic interpretation by drawing on information from multiple levels within the model. Rather than relying on predefined concept labels, MCPNet autonomously discovers meaningful concepts from feature maps. To increase versatility, we further propose MCPNet++, which can be seamlessly applied to both CNN and transformer backbones, allowing it to learn meaningful concepts from their respective features. Building on these learned concepts, we also introduce a large language model (LLM)-based method to bridge the gap between these concepts and human perception. Experimental results show that MCPNet++ provides more comprehensive explanations without sacrificing model performance, with the discovered concepts aligning closely with human understanding.

Recent advancements in post-hoc and inherently inter-pretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. Although being effective in bridging the semantic gap between a model's latent space and human interpretation, these explanation methods only partially reveal the model's decision-making process. The outcome is typically limited to high-level semantics derived from the last feature map. We argue that the expla-nations lacking insights into the decision processes at low and mid-level features are neither fully faithful nor useful. Addressing this gap, we introduce the Multi-Level Concept Prototypes Classifier (MCPNet), an inherently interpretable model. MCPNet autonomously learns meaningful concept prototypes across multiple feature map levels using Cen-tered Kernel Alignment (CKA) loss and an energy-based weighted PCA mechanism, and it does so without reliance on predefined concept labels. Further, we propose a novel classifier paradigm that learns and aligns multilevel concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss. Our experiments reveal that our proposed MCPNet while being adapt-able to various model architectures, offers comprehensive multilevel explanations while maintaining classification accuracy. Additionally, its concept distribution-based classification approach shows improved generalization capabilities in few-shot classification scenarios. Project page is available11https://eddie221.github.io/MCPNet/.

Change detection (CD) aims to find the difference between two images at different times and output a change map to represent whether the region has changed or not. To achieve a better result in generating the change map, many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. However, these methods still get lower performance because they ignore spatial information and scaling changes between objects, giving rise to blurry boundaries. In addition to these, they also neglect the interactive information of two different images. To alleviate these problems, we propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue. In this paper, three modules are proposed that include relation-aware, scale-aware, and cross-transformer to tackle the problem of scene change detection more effectively. To verify our model, we tested three public datasets, including LEVIR-CD, WHU-CD, and DSFIN, and obtained SoTA accuracy. Our code is available at https://github.com/f64051041/SARAS-Net.

It is challenging to classify patterns with small inter-class variations but large intra-class variations especially for textured objects with relatively small sizes and blurry boundaries. We propose the Co-Feature Network (COFENet), a novel deep learning network for fine-grained texture-based image classification. State-of-the-art (SoTA) methods on this mostly rely on feature concatenation by merging convolutional features into fully connected layers. Some existing work explored the variation between pair-wise features during learning, they only considered the relations in the feature channels, and did not explore the spatial or structural relations among the image regions where the features are extracted from. We propose to leverage such information among the features and their relative spatial layouts to capture richer pairwise, orientationwise, and distancewise relations among feature channels for end-to-end learning of intra-class and inter-class variations.