Compositional zero-shot learning (CZSL) strives to learn attributes and objects from seen compositions and transfer the acquired knowledge to unseen compositions. Existing methods either learn primitive concepts in an entangled manner, leading to the model relying on spurious correlations between attribute...
Reference Paper:Learning to Compose Soft Prompts for Compositional Zero-Shot Learning Setup conda create --name clip python=3.7 conda activate clip pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 pip3 install ftfy regex tqdm scipy pandas pip3 in...
CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning Zhaoheng Zheng, Haidong Zhu and Ram Nevatia Official implementation of CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning. Installation We build our model based on Python 3.8 and PyTorch 1.13. ...
This work explores the zero-shot compositional learning ability of large pre-trained vision-language models(VLMs) within the prompt-based learning framework and propose a model (\textit{PromptCompVL}) to solve the compositonal zero-shot learning (CZSL) problem. \textit{PromptCompVL} makes two ...
(including the action prompt,object prompt,and procedure prompt),which could compositionally distill knowledge from short-term video-language models to facilitate long-term procedure understanding.Besides,the task reformulation enables our CPL to perform well in all zero-shot,few-shot,and fully-...
Zero-Shot Learning—A Comprehensive Eval- uation of the Good, the Bad and the Ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. [67] Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-Shot Learning — The Good, the Bad and the Ug...
These tasks are - (i) compositional visual question answering; (ii) zero-shot natural language visual reasoning (NLVR) on im- age pairs; (iii) factual knowledge object tagging from natu- ral language instructions; and (iv) language-guided image editing. We emphasize...
While supervised approaches rely on annotating triplets that is costly (i.e. query image, textual modification, and target image), recent research sidesteps this need by using large-scale vision-language models (VLMs), performing Zero-Shot CIR (ZS-CIR). However, state-of-the-art approaches ...
In this turn, and with enough data, we can gradually transition between general purpose LLMs with zero and few-shot learning capabilities, and specialized fine-tuned models to solve specific problems (see above). This means that each operations could be designed to use a model with fine-tuned...
1. Introduction Vision-language models (VLMs) have achieved high performance on various downstream tasks, including many zero-shot learning and text-guided vision tasks [2, 4, 19, *Work was done during the author's internship at Meituan. †Corresponding...