The paper introduces PromptCCZSL, a framework that enables vision‑language models to continually learn new attributes, objects, and their unique compositions while avoiding forgetting. It uses a frozen VLM backbone with prompt‑based techniques, recency‑weighted multi‑teacher distillation, and several loss functions (CAL, OPL, IDL) to maintain prior knowledge and promote diverse, distinct embeddings. Experiments on UT‑Zappos and C‑GQA show significant performance gains over existing VLM‑based and non‑VLM baselines, establishing a new benchmark for continual compositional zero‑shot learning.
By Sauda Maryam, Sara Nadeem, Faisal Qureshi, Mohsen Ali
arXiv:2607. 22919v1 Announce Type: cross Abstract: Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification.
By Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani, Mubarak Shah, Kushal Kafle
The paper introduces CLEAR, a CLoze-style rEAsoning-based Re-ranking framework for Compositional Zero-Shot Learning. CLEAR treats primitive variations as context-driven activations of concrete visual cues rather than independent entities, extracting conditional variants in a coarse-to-fine manner and performing cloze-style reasoning to infer high-level semantics. Experiments show that CLEAR consistently improves base models and surpasses state-of-the-art methods on the C-GQA and MIT-States datasets.
By Weize Li, Zhicheng Zhao, Fei Su
UniFGVC introduces a training‑free framework for few‑shot fine‑grained visual classification that reframes the task as a multimodal retrieval problem. It uses a Category‑Discriminative Visual Captioner to generate attribute‑aware textual descriptions, then matches image‑description pairs with category templates via pre‑trained encoders. Experiments on 12 datasets show that UniFGVC consistently outperforms existing CLIP‑based and fully‑supervised MLLM methods, demonstrating the effectiveness of the retrieval paradigm.
By Haiyun Guo, Hongyu Guo, Xiangzhao Hao, Jiarui Guo, Jinqiao Wang, Tat-Seng Chua
arXiv:2607. 00374v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification.
By Jingjing Zhang, Lei Zhang, Zheren Fu, Zhendong Mao
arXiv:2608.12088v2 Announce Type: replace
Abstract: Generative models can produce images nearly indistinguishable from real data, yet rigorous and interpretable evaluation remains challenging. Conven...
By Yifan Lu, Taras Kucherenko, Hedvig Kjellstr\"om, Judith B\"utepage