24 papers · ranked by Valyu relevance
Siqi Yin, Lifan Jiang, Sushank Chaudhary
Zero-shot image classification enables the recognition of new categories without requiring additional training data, thereby enhancing the model’s generalization capability when specific training are unavailable. This paper introduces a zero-shot image classification framework to recognize new categories that are…
Huabo Shen, Xiaodong Sun, Youmin Hu, Changgeng Li + 3 more
Zero-shot learning (ZSL) aims to classify unseen classes by leveraging semantic information from seen classes, addressing the challenge of limited labeled data. In recent years, ZSL methods have focused on extracting attribute-level features from images and aligning them with semantic features within an embedding…
Emine Ayşe Sunar, Zeynep Işık, Mert Pekey, Ramazan Gökberk Cinbiş + 1 more
Protein Language Models (pLMs) have emerged as powerful tools for capturing the intricate information encoded in protein sequences, facilitating various downstream protein prediction tasks. With numerous pLMs available, there is a critical need for diverse benchmarks to systematically evaluate their performance across…
Sima Ataei, Gregory Butler
Transmembrane transport proteins mediate selective movement of ions and metabolites across membranes. Experimental characterization of their substrate specificity is limited. For novel class discovery with limited data, zero-shot learning aims to assign labels to test samples whose label has not been seen previously…
Beibei Yu, Cheng Xie, Peng Tang, Bin Li + 1 more
Almost all existing zero-shot learning methods work only on benchmark datasets (e.g., CUB, SUN, AwA, FLO and aPY) which have already provided pre-defined attributes for all the classes. These methods thus are hard to apply on real-world datasets (like ImageNet) since there are no such pre-defined attributes in the data…
Nanda Nanduri, Jesutomiwo Ogundare, George Anderson
Camera trap networks such as Snapshot Safari have generated millions of labelled wildlife images across Africa, enabling the training of deep learning models for automated species classification. However, deploying models trained in one African region to another remains poorly understood. To the best of our knowledge…
Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch + 3 more
'Ghouthi Boukli Hacene' 'Fabien Cardinaux' 'Vincent Gripon'] We consider the problem of zero-shot one-class visual classification, extending traditional one-class classification to scenarios where only the label of the target class is available. This method aims to discriminate between positive and negative query…
Mei-Chen Yeh, Fang Li
We present a new embedding-based framework for zero-shot learning (ZSL). Most embedding-based methods aim to learn the correspondence between an image classifier (visual representation) and its class prototype (semantic representation) for each class. Motivated by the binary relevance method for multi-label…
Alix Auzepy, Elena Tönjes, David Lenz, Christoph Funk + 1 more
'Sanaa Kaddoura'] We examine climate-related disclosures in a large sample of reports published by banks that officially endorsed the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD). In doing so, we introduce a new application of the zero-shot text classification. By developing a set…
Liangwei Li, Lin Liu, Xiaohui Du, Xiangzhou Wang + 5 more
'Jing Zhang' 'Ping Zhang' 'Juanxiu Liu' 'Paweł Pławiak'] Taxonomy illustrates that natural creatures can be classified with a hierarchy. The connections between species are explicit and objective and can be organized into a knowledge graph (KG). It is a challenging task to mine features of known categories from KG and…
Thomas P. O’Connell, Marvin M. Chun, Gabriel Kreiman
Decoding information from neural responses in visual cortex demonstrates interpolation across repetitions or exemplars. Is it possible to decode novel categories from neural activity without any prior training on activity from those categories? We built zero-shot neural decoders by mapping responses from macaque…
Huang Xie, Okko Räsänen, Tuomas Virtanen
In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. Zeroshot learning in audio classification refers to classification problems that aim at recognizing audio instances of sound classes, which…
Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang + 5 more
Classification via Anchor Generation and Classification Reframing Authors: ['Han Liu' 'Siyang Zhao' 'Xiaotong Zhang' 'Feng Zhang' 'Wei Wang' 'Fenglong Ma' 'Hongyang Chen' 'Yu Hong' 'Xianchao Zhang'] Few-shot and zero-shot text classification aim to recognize samples from novel classes with limited labeled samples or no…
Moe Matsuki, Paula Lago, Sozo Inoue
In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors.…
Huajie Jiang, Ruiping Wang, Shiguang Shan, Xilin Chen
> Abstract. Zero-shot learning (ZSL) aims to recognize objects of novel classes without any training samples of specific classes, which is achieved by exploiting the semantic information and auxiliary datasets. Recently most ZSL approaches focus on learning visual-semantic embeddings to transfer knowledge from the…
Authors not listed
Molecular property prediction is a fundamental task in computational chemistry with critical applications in drug discovery and materials science. While recent works have explored Large Language Models (LLMs) for this task, they primarily rely on textual molecular representations such as SMILES/SELFIES, which can be…
Tianyuan Liu, Tingze Feng, Xianrun Pan, Yangyang Chen + 5 more
Single-cell foundation models (scFMs) have been proposed as reusable representations for transcriptomic analysis, yet their practical utility and robustness when applied without task-specific fine-tuning remain incompletely characterized. Here, we systematically evaluated single-cell transcriptomic representations in…
Zhenyong Fu, Tao Xiang, Shaogang Gong
Zero-shot learning aims to classify visual objects without any training data via knowledge transfer between seen and unseen classes. This is typically achieved by exploring a semantic embedding space where the seen and unseen classes can be related. Previous works differ in what embedding space is used and how…
Qian Wang, Ke Chen
Zero-shot learning for visual recognition, e.g., object and action recognition, has recently attracted a lot of attention. However, it still remains challenging in bridging the semantic gap between visual features and their underlying semantics and transferring knowledge to semantic categories unseen during learning.…
Yanwei Fu, Tao Xiang, Yu‐Gang Jiang, Xiangyang Xue + 2 more
'Shaogang Gong'] Abstract—With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Rachana Niranjan Murthy, Sai Teja Potu, Akhil Thomas, Lokesh Mishra + 2 more
Retrieving structured materials information from unstructured textual data is essential for data mining and automatically developing comprehensive ontologies. Information extraction is a complex task composed of multiple subtasks and thus often relies on systems of task-specialized language models. A foundation…
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen + 2 more
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously…