Compositional Zero-Shot Learning (CZSL) addresses the challenge of recognizing unseen attribute-object compositions in images, representing a fundamental challenge in artificial intelligence. Current approaches, which primarily focus on semantic alignment or distribution independence of primitives, have not achieved effective state-object decoupling and causal interventional invariance, limiting their performance on unseen compositions. To tackle this challenge, this study introduces I2CD (Invertible Causal framework via Disentangle-Compose-Disentangle), a novel framework that integrates invertible neural networks with causal intervention techniques to achieve state-object disentanglement. The framework employs a disentangle-compose-disentangle mechanism for counterfactual generation within the disentangled representation space, ensuring that modifications to one primitive (attribute or object) maintain independence from the other, thus enabling robust causal disentanglement. Representational consistency is maintained through semantic alignment between initial disentangled representations and their recomposed-then-disentangled counterparts with corresponding textual concepts. Comprehensive evaluations on three benchmark datasets—MIT-States, UT-Zappos, and C-GQA—demonstrate the framework's effectiveness in achieving both disentanglement and compositional generalization in CZSL tasks.

Open-World Object Detection (OWOD) aims to detect unseen objects as “unknown” while incrementally learning them without catastrophic forgetting. This problem presents two major challenges: (1) the lack of annotations for unknown objects during training, and (2) the risk of catastrophic forgetting during model updates. To address these issues, we propose the COmpositional and Bidirectional low-Rank Adaptive open-world detection transformer (COBRA)-a novel framework built upon a pre-trained Deformable DETR model. Specifically, COBRA first employs an attentional filtering mechanism that prunes previously known (P-Known) and currently known (C-Known) objects, yielding a purified set of candidateunknowns. To system-atically pseudo-label theseunknowns, we introduce a Primitive Composition Recognition (PCR) module, which evaluates set-level similarity between candidate objects and learned primitives, enabling accurate labeling ofpseudo-unknowns. To mitigate catastrophic forgetting during incremental updates, COBRA leverages Bidirectional Low-Rank Adaptation (Bi-LoRA)-a parameter-efficient mechanism that supports forward knowledge transfer and stable backward integration. Together, these components form a synergistic pipeline for continual object discovery and knowledge consolidation. Extensive experiments on MS COCO and PASCAL VOC demonstrate that our rehearsal-free COBRA framework outperforms SAM-powered methods in unknown recall while achieving lower forgetting compared to rehearsal-based competitors.

Class incremental learning (CIL) requires models to acquire knowledge from sequential tasks containing non-overlapping classes while avoiding catastrophic forgetting. While vision-language foundation models like CLIP demonstrate remarkable potential for CIL through their pre-trained cross-modal alignment capabilities, existing CLIP-based approaches critically overlook the progressive degradation of visual representations in incremental scenarios . Through feature space analysis, we identify a crucial dichotomy : textual embeddings maintain stable discriminative power across sequential tasks, whereas visual features exhibit progressive deterioration manifested by intra-task confusion (ambiguous decision boundaries between co-occurring classes) and inter-task interference (semantic collision between historical and novel categories). To address these dual challenges, we propose task-guided hierarchical multi-modal alignment (THMM-CLIP), a framework that establishes persistent visual-textual coherence through hierarchical multi-modal alignment (HMA) and robust prompt selection (RPS). HMA adapts lightweight task-specific prompt vectors to dynamically recalibrate the CLIP image encoder, thereby achieving: (i) intra-task alignment, (ii) inter-task discriminability alignment, and (iii) global structural alignment with textual features. RPS incorporates a dual-level task identifier that integrates class-level and task-level representative features to ensure precise prompt retrieval during inference. Ablation studies validate all components’ contributions, while t-SNE visualizations, confusion matrices, and Grad-CAM analyses confirm strengthened cross-modal alignment.