Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 122 for “"zero-shot"”.
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On Zero-Shot Reinforcement Learning
… with this misalignment is the primary concern of zero-shot reinforcement learning, a problem setting where the agent must generalise to a new task or domain with zero practice shots. Whilst impressive progress has been made on methods that perform zero-shot RL in idealised settings, new work is …
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Generalization and Specialization in Zero-Shot Learning
… of deep models. To overcome these obstacles, Zero-Shot Learning (ZSL) is proposed. ZSL aims to transfer classification ability from seen to unseen classes with semantic side information as the bridge. The success of ZSL requires two crucial abilities, i.e., the generalization ability to …
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Concept Vectors for Zero-Shot Video Generation
Zero-shot video generation involves generating videos of concepts (action classes) that are not seen in the training phase. Even though the research community has explored conditional video generation for long high-resolution videos, zero-shot video remains a fairly unexplored and challenging task. …
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Optimising image feature zero-shot-learning with EEG
… not present during training, i.e. we apply zero-shot learning.<br/><br/>To achieve this goal, we first investigate a discriminative feature extraction process for EEG brain data and establish a baseline evaluation of exemplar and category-level decoding. Once competitive decoding rates are …
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Zero-shot learning to execute tasks with robots
… with several features, namely: allowing for zero-shot learning agents that are robust and resilient in the event of failing midway during a task, allowing us to detect failures, and being highly generalizable to new environments. Initially, we focused mostly on training agents that are …
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Commonsense for Zero-Shot Natural Language Video Localization
Zero-shot Natural Language-Video Localization (NLVL) has shown promising results in training NLVL models solely with raw video data through dynamic video segment proposal generation and pseudo-query annotations. However, existing pseudo-queries lack grounding in the source video and suffer from a …
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Zero-Shot Scene Graph Relationship Prediction using VLMs
… recent surge of interest in open-vocabulary and zero-shot SGG, most approaches still require some form of training or adaptation on the target dataset, even when using Vision-Language Models (VLMs). In this work, we propose a training-free framework for the VLMs to predict scene graph …
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Zero-shot object-goal navigation using multimodal goal embeddings
… (e.g., "find a sink"). The approach is entirely zero-shot - i.e., it does not require ObjectNav rewards or demonstrations of any kind. Instead, we train on the image-goal navigation (ImageNav) task, in which agents find the location where a picture (i.e., goal image) was captured. Specifically, …
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Class Representative Projection for Text-based Zero-Shot Learning
… classification. Transfer learning, known as the zero-shot learning (ZSL) or generalized zero-shot learning (G-ZSL), is receiving much attention due to its ability to transfer knowledge learned from a known (seen) domain to unknown (unseen) domains. But most of the ZSL works are relying on large …
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Tailoring large language models for zero-shot relation extraction
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Zero-Shot Low Light Image Enhancement with Diffusion Prior
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Grounded SCAN Human: A Benchmark for Zero-Shot Generalizations
… during train time. During test time, models must zero-shot execute commands that require the agent to move in new directions, commands that contain novel combinations of objects and adjectives, and other such generalizations in different test sets called splits. However, gSCAN does not contain …
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Few-Shot and Zero-Shot Learning for Information Extraction
… extraction in e-commerce, with few labeled (few-shot learning) or even no labeled (zero-shot learning) training data. We explore multi-source auxiliary information and novel learning techniques to integrate semantic auxiliary information with the input text to improve few-shot learning and …
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Mid-level representations for action recognition and zero-shot learning
… and the other two problems in the task of zero-shot learning. For the first problem, we devise a representation suitable for characterising human actions on the basis of a sequence of pose estimates generated by an RGB-D sensor. We show that discriminate sequence of poses typically occur …
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Deep Attentional Modulation for Zero-shot Learning in Object Recognition
… to recognize objects from just a description (zero-shot) or a few examples (few-shot). Traditionally, artificial neural networks have struggled at reproducing this ability, with large performance drops in the zero-and few-shot domains caused by overfitting. Most methods are focusing on learning …
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Speaker Anonymization using End-to-End Zero-Shot Voice Conversion
… introduce a model for performing end-to-end zero-shot voice conversion by modifying the architecture of a neural vocoder. To the best of our knowledge, this is one of the first end-to-end approaches for zero-shot VC that has ever been proposed. Our model is able to maintain the clarity and …
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Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
… generalize across problem landscapes. We present ZeroShotOpt, the first general-purpose, pretrained model for continuous black-box optimization tasks ranging from 2 D to 20 D. Our approach leverages offline reinforcement learning on large-scale optimization trajectories collected from 12 BO …
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Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images
… or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of image-text pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable …
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Autonomous Mobile Robot Navigation in Dynamic Real-World Environments Without Maps With Zero-Shot Deep Reinforcement Learning
… at physical limits in simulation, transferred zero-shot to the real-world for robust end-to-end AMR navigation. The representation learned in a compact parameter space with 2 fully connected layers with 64 nodes each is demonstrated to exhibit emergent behavior for Out-of-Distribution (OOD) …
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