Zero-shot classification for structured tabular data has attracted increasing attention in recent years. An important line of research is leveraging textual explanations as supervision to improve the modeling of tabular attribute features. Previous studies usually serialize tabular data into flattened explanation sequences, or employ logical rules to model directional associations between tabular attributes and labels. However, these methods often suffer from two major limitations: the former disrupts the inherent row-column topological structure of tables, while the latter mainly represents relations with fixed logical operators or aggregation weights, making it difficult to model learnable dependencies among features. To address these issues, we propose a novel method (MulEG), which harnesses multi-level textual explanations with a feature attribute graph to model both tabular topology and cell-element relations. Specifically, to preserve the intrinsic row-column topological structure of tables, we propose a multi-level textual explanation modeling with sample-level, attribute-level, and label-level perspectives. Moreover, to capture fine-grained feature dependencies, we propose a differentiable message-passing mechanism using a feature attribute graph that allows heterogeneous features to propagate through message passing. Finally, extensive experimental results on datasets covering sixteen tasks demonstrate the effectiveness of the proposed approach.

Object categories are typically organized into a multi-granularity taxonomic hierarchy. When classifying categories at different hierarchy levels, traditional uni-modal approaches focus primarily on image features, revealing limitations in complex scenarios. Recent studies integrating Vision-Language Models (VLMs) with class hierarchies have shown promise, yet they fall short of fully exploiting the hierarchical relationships. These efforts are constrained by their inability to perform effectively across varied granularity of categories. To tackle this issue, we propose a novel framework (HGCLIP) that effectively combines CLIP with a deeper exploitation of the Hierarchical class structure via Graph representation learning. We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category. After passing through a graph encoder, the textual features incorporate hierarchical structure information, while the image features emphasize class-aware features derived from prototypes through the attention mechanism. Our approach demonstrates significant improvements on 11 diverse visual recognition benchmarks. Our codes are fully available at https://github.com/richard-peng-xia/HGCLIP.