The field of computer vision is witnessing significant advancements in zero-shot learning and fine-grained visual recognition. Researchers are exploring innovative approaches to improve the performance of deep learning models in these areas. One notable direction is the development of methods that can learn category-agnostic representations, enabling robust fine-grained generalization and zero-shot recognition. Another area of focus is the use of language modeling and vision-language models to enhance the interpretability and scalability of image classification systems. These advancements have the potential to make significant impacts in real-world applications, such as smart homes and medical diagnosis. Noteworthy papers in this area include: CXR-CML, which proposes a class-weighting mechanism to improve zero-shot classification of long-tailed multi-label diseases in Chest X-Rays, achieving a 7% improvement in zero-shot AUC scores. Adversarial Reconstruction Feedback for Robust Fine-grained Generalization proposes a novel framework for learning category-agnostic discrepancy representations, demonstrating impressive performance on fine-grained and coarse-grained datasets. Vocabulary-free Fine-grained Visual Recognition via Enriched Contextually Grounded Vision-Language Model presents a training-free method that offers state-of-the-art results in fine-grained visual recognition, with strong potential in real-world scenarios and new domains.