Yi-Zhe Song - IEEE Xplore Author Profile
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Yi-Zhe Song

Also published under: Yizhe Song, YiZhe Song

Publications
120
Citations
4,370
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (148)
Image of author Yi-Zhe Song

Yi-Zhe Song

Also published under: Yizhe Song, YiZhe Song

Affiliation

SketchX
CVSSP
University of Surrey, Guildford, United Kingdom

Publication Topics

Biography

Yi-Zhe Song (Senior Member, IEEE) received the bachelor's degree (First Class Honours) from the University of Baththe, in 2003, the MSc degree (with Best Dissertation Award) from the University of Cambridge, in 2004, and the PhD degree in computer vision and machine learning from the University of Bath, in 2008. He is a chair professor of computer vision and machine learning, and director with SketchX Lab, Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey. He is an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and International Journal of Computer Vision. He was a program chair for British Machine Vision Conference (BMVC) 2021, and regularly serves as area chair for fl...
Publications
120
Citations
4,370
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (148)
Author's Published Works

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Adaptive Multi-Resolution Feature Fusion for Fine-Grained Visual Classification

Yuqi Yang;Dongliang Chang;Ruoyi Du;Yi-Zhe Song;Zhanyu Ma

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2025 | Early Access Article |
Despite significant progress, the shortage of labeled data and expert knowledge remains a challenge for Fine-grained Visual Classification (FGVC). Some multi-source approaches that incorporate additional modalities, such as sound or bounding boxes, show promise for data enrichment but introduce added complexity to data collection. In this paper, we pose the question: can multi-source capabilities ...Show More

Adaptive Multi-Resolution Feature Fusion for Fine-Grained Visual Classification

Yuqi Yang;Dongliang Chang;Ruoyi Du;Yi-Zhe Song;Zhanyu Ma

Year: 2025 | Early Access Article |

Reserve to Adapt: Mining Inter-Class Relations for Open-Set Domain Adaptation

Yujun Tong;Dongliang Chang;Da Li;Xinran Wang;Kongming Liang;Zhongjiang He;Yi-Zhe Song;Zhanyu Ma

IEEE Transactions on Image Processing
Year: 2025 | Volume: 34 | Journal Article |
Open-Set Domain Adaptation (OSDA) aims at adapting a model trained on a labelled source domain, to an unlabeled target domain that is corrupted with unknown classes. The key challenge inherent to this open-set setting is therefore how best to avoid the negative transfer incurred by unknown classes during model adaptation. Most existing works tackle this challenge by simply pushing the entire unkno...Show More

Reserve to Adapt: Mining Inter-Class Relations for Open-Set Domain Adaptation

Yujun Tong;Dongliang Chang;Da Li;Xinran Wang;Kongming Liang;Zhongjiang He;Yi-Zhe Song;Zhanyu Ma

Year: 2025 | Volume: 34 | Journal Article |

Understanding Episode Hardness in Few-Shot Learning

Yurong Guo;Ruoyi Du;Aneeshan Sain;Kongming Liang;Yuan Dong;Yi-Zhe Song;Zhanyu Ma

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Volume: 47, Issue: 1 | Journal Article |
Achieving generalization for deep learning models has usually suffered from the bottleneck of annotated sample scarcity. As a common way of tackling this issue, few-shot learning focuses on “episodes”, i.e., sampled tasks that help the model acquire generalizable knowledge onto unseen categories – better the episodes, the higher a model's generalisability. Despite extensive research, the character...Show More

Understanding Episode Hardness in Few-Shot Learning

Yurong Guo;Ruoyi Du;Aneeshan Sain;Kongming Liang;Yuan Dong;Yi-Zhe Song;Zhanyu Ma

Year: 2025 | Volume: 47, Issue: 1 | Journal Article |

ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

Kam Woh Ng;Xiatian Zhu;Yi-Zhe Song;Tao Xiang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are autom...Show More

ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

Kam Woh Ng;Xiatian Zhu;Yi-Zhe Song;Tao Xiang

Year: 2024 | Conference Paper |

Wired Perspectives: Multi-View Wire Art Embraces Generative AI

Zhiyu Qu;Lan Yang;Honggang Zhang;Tao Xiang;Kaiyue Pang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists. In response, we present DreamWire, an AI system enabling everyone to craft MVWA easily. Users express their vision through text prompts or scribbles, freeing them from intricate 3D wire organisation. Our approach synergises 3D Bézier curves,...Show More

Wired Perspectives: Multi-View Wire Art Embraces Generative AI

Zhiyu Qu;Lan Yang;Honggang Zhang;Tao Xiang;Kaiyue Pang;Yi-Zhe Song

Year: 2024 | Conference Paper |

Doodle Your 3D: from Abstract Freehand Sketches to Precise 3D Shapes

Hmrishav Bandyopadhyay;Subhadeep Koley;Ayan Das;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
In this paper, we democratise 3D content creation, enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills. We introduce a novel part-level modelling and alignment framework that facilitates abstraction modelling and cross-modal correspondence. Leveraging the same part-level decoder, our approach seamlessly extends to sketch modelling by ...Show More

Doodle Your 3D: from Abstract Freehand Sketches to Precise 3D Shapes

Hmrishav Bandyopadhyay;Subhadeep Koley;Ayan Das;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

Year: 2024 | Conference Paper |

How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (6)
In this paper, we propose a novel abstraction-aware sketch-based image retrieval framework capable of handling sketch abstraction at varied levels. Prior works had mainly focused on tackling sub-factors such as drawing style and order, we instead attempt to model abstraction as a whole, and propose feature-level and retrieval granularity-level designs so that the system builds into its DNA the nec...Show More

How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

Year: 2024 | Conference Paper |

You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
Two primary input modalities prevail in image retrieval: sketch and text. While text is widely used for inter-category retrieval tasks, sketches have been established as the sole preferred modality for fine-grained image retrieval due to their ability to capture intricate visual details. In this paper, we question the reliance on sketches alone for fine-grained image retrieval by simultaneously ex...Show More

You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

Year: 2024 | Conference Paper |

It's All About Your Sketch: Democratising Sketch Control in Diffusion Models

Subhadeep Koley;Ayan Kumar Bhunia;Deeptanshu Sekhri;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI. We importantly democratise the process, enabling amateur sketches to generate precise images, living up to the commitment of “what you sketch is what you get”. A pilot study underscores the necessity, revealing that deformities in existing models stem from...Show More

It's All About Your Sketch: Democratising Sketch Control in Diffusion Models

Subhadeep Koley;Ayan Kumar Bhunia;Deeptanshu Sekhri;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

Year: 2024 | Conference Paper |

What Sketch Explainability Really Means for Downstream Tasks?

Hmrishav Bandyopadhyay;Pinaki Nath Chowdhury;Ayan Kumar Bhunia;Aneeshan Sain;Tao Xiang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond explanations of network behavior, we discern the genuine implications of explainability across diverse downstream sketch-related tasks. We propose a lightweight and portable explainability solution - a seamless plugin t...Show More

What Sketch Explainability Really Means for Downstream Tasks?

Hmrishav Bandyopadhyay;Pinaki Nath Chowdhury;Ayan Kumar Bhunia;Aneeshan Sain;Tao Xiang;Yi-Zhe Song

Year: 2024 | Conference Paper |

SketchINR: A First Look into Sketches as Implicit Neural Representations

Hmrishav Bandyopadhyay;Ayan Kumar Bhunia;Pinaki Nath Chowdhury;Aneeshan Sain;Tao Xiang;Timothy Hospedales;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (3)
We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the xy point coordinates in a sketch at each time and stroke. Despite its simplicity, SketchINR outperfor...Show More

SketchINR: A First Look into Sketches as Implicit Neural Representations

Hmrishav Bandyopadhyay;Ayan Kumar Bhunia;Pinaki Nath Chowdhury;Aneeshan Sain;Tao Xiang;Timothy Hospedales;Yi-Zhe Song

Year: 2024 | Conference Paper |

DemoFusion: Democratising High-Resolution Image Generation With No $$$

Ruoyi Du;Dongliang Chang;Timothy Hospedales;Yi-Zhe Song;Zhanyu Ma

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (7)
High-resolution image generation with Generative Artificial Intelligence (GenAl) has immense potential but, due to the enormous capital investment required for training, it is increasimgly centralised to a few large corporations, and hidden behind paywalls. This paper aims to democratise high-resolution GenAl by advancing the frontier of high-resolution generation while remaining accessible to a b...Show More

DemoFusion: Democratising High-Resolution Image Generation With No $$$

Ruoyi Du;Dongliang Chang;Timothy Hospedales;Yi-Zhe Song;Zhanyu Ma

Year: 2024 | Conference Paper |

Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (4)
This paper, for the first time, explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos. This proficiency is underpinned by their robust cross-modal capabilities and shape bias, findings that are substantiated through our pi...Show More

Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

Year: 2024 | Conference Paper |

DemoCaricature: Democratising Caricature Generation with a Rough Sketch

Dar-Yen Chen;Ayan Kumar Bhunia;Subhadeep Koley;Aneeshan Sain;Pinaki Nath Chowdhury;Yi-Zhe Song

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch. Our objective is to strike a delicate balance between abstraction and identity, while preserving the creativity and subjectivity inherent in a sketch. To achieve this, we present Explicit Rank-1 Model Editing alongside single-image pe...Show More

DemoCaricature: Democratising Caricature Generation with a Rough Sketch

Dar-Yen Chen;Ayan Kumar Bhunia;Subhadeep Koley;Aneeshan Sain;Pinaki Nath Chowdhury;Yi-Zhe Song

Year: 2024 | Conference Paper |

3D Reconstruction From a Single Sketch via View-Dependent Depth Sampling

Chenjian Gao;Xilin Wang;Qian Yu;Lu Sheng;Jing Zhang;Xiaoguang Han;Yi-Zhe Song;Dong Xu

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 12 | Journal Article |
Reconstructing a 3D shape based on a single sketch image is challenging due to the inherent sparsity and ambiguity present in sketches. Existing methods lose fine details when extracting features to predict 3D objects from sketches. Upon analyzing the 3D-to-2D projection process, we observe that the density map, characterizing the distribution of 2D point clouds, can serve as a proxy to facilitate...Show More

3D Reconstruction From a Single Sketch via View-Dependent Depth Sampling

Chenjian Gao;Xilin Wang;Qian Yu;Lu Sheng;Jing Zhang;Xiaoguang Han;Yi-Zhe Song;Dong Xu

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

CreativeSeg: Semantic Segmentation of Creative Sketches

Yixiao Zheng;Kaiyue Pang;Ayan Das;Dongliang Chang;Yi-Zhe Song;Zhanyu Ma

IEEE Transactions on Image Processing
Year: 2024 | Volume: 33 | Journal Article |
Cited by: Papers (1)
The problem of sketch semantic segmentation is far from being solved. Despite existing methods exhibiting near-saturating performances on simple sketches with high recognisability, they suffer serious setbacks when the target sketches are products of an imaginative process with high degree of creativity. We hypothesise that human creativity, being highly individualistic, induces a significant shif...Show More

CreativeSeg: Semantic Segmentation of Creative Sketches

Yixiao Zheng;Kaiyue Pang;Ayan Das;Dongliang Chang;Yi-Zhe Song;Zhanyu Ma

Year: 2024 | Volume: 33 | Journal Article |

Bi-Directional Ensemble Feature Reconstruction Network for Few-Shot Fine-Grained Classification

Jijie Wu;Dongliang Chang;Aneeshan Sain;Xiaoxu Li;Zhanyu Ma;Jie Cao;Jun Guo;Yi-Zhe Song

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 9 | Journal Article |
Cited by: Papers (2)
The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting – a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variatio...Show More

Bi-Directional Ensemble Feature Reconstruction Network for Few-Shot Fine-Grained Classification

Jijie Wu;Dongliang Chang;Aneeshan Sain;Xiaoxu Li;Zhanyu Ma;Jie Cao;Jun Guo;Yi-Zhe Song

Year: 2024 | Volume: 46, Issue: 9 | Journal Article |

Mind the Gap: Open Set Domain Adaptation via Mutual-to-Separate Framework

Dongliang Chang;Aneeshan Sain;Zhanyu Ma;Yi-Zhe Song;Ruiping Wang;Jun Guo

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Volume: 34, Issue: 6 | Journal Article |
Cited by: Papers (1)
Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain. Amongst its many variants, open set domain adaptation (OSDA) is perhaps the most challenging one, as it further assumes the presence of unknown classes in the target domain. In this paper, we study OSDA with a particular focus on enriching its ability to traverse ...Show More

Mind the Gap: Open Set Domain Adaptation via Mutual-to-Separate Framework

Dongliang Chang;Aneeshan Sain;Zhanyu Ma;Yi-Zhe Song;Ruiping Wang;Jun Guo

Year: 2024 | Volume: 34, Issue: 6 | Journal Article |

Semi-Supervised Learning for FGVC With Out-of-Category Data

Ruoyi Du;Dongliang Chang;Zhanyu Ma;Kongming Liang;Yi-Zhe Song;Jun Guo

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 5 | Journal Article |
Cited by: Papers (2)
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are called for. Semi-supervised learning (SSL) techniques, acquiring knowledge from unlabeled data, provide a considerable means forward and have shown great promise for coarse-grained problems. However, exiting SSL paradigms mos...Show More

Semi-Supervised Learning for FGVC With Out-of-Category Data

Ruoyi Du;Dongliang Chang;Zhanyu Ma;Kongming Liang;Yi-Zhe Song;Jun Guo

Year: 2024 | Volume: 46, Issue: 5 | Journal Article |

Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion

Xiao Han;Xiatian Zhu;Jiankang Deng;Yi-Zhe Song;Tao Xiang

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
Controllable person image synthesis aims at rendering a source image based on user-specified changes in body pose or appearance. Prior art approaches leverage pixel-level denoising diffusion models conditioned on the coarse skeleton via cross-attention. This leads to two limitations: low efficiency and inaccurate condition information. To address both issues, a novel Pose-Constrained Latent Diffus...Show More

Controllable Person Image Synthesis with Pose-Constrained Latent Diffusion

Xiao Han;Xiatian Zhu;Jiankang Deng;Yi-Zhe Song;Tao Xiang

Year: 2023 | Conference Paper |

Task-aware Adaptive Learning for Cross-domain Few-shot Learning

Yurong Guo;Ruoyi Du;Yuan Dong;Timothy Hospedales;Yi-Zhe Song;Zhanyu Ma

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
Although existing few-shot learning works yield promising results for in-domain queries, they still suffer from weak cross-domain generalization. Limited support data requires effective knowledge transfer, but domain-shift makes this harder. Towards this emerging challenge, researchers improved adaptation by introducing task-specific parameters, which are directly optimized and estimated for each ...Show More

Task-aware Adaptive Learning for Cross-domain Few-shot Learning

Yurong Guo;Ruoyi Du;Yuan Dong;Timothy Hospedales;Yi-Zhe Song;Zhanyu Ma

Year: 2023 | Conference Paper |

Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting

Pinaki Nath Chowdhury;Ayan Kumar Bhunia;Aneeshan Sain;Subhadeep Koley;Tao Xiang;Yi-Zhe Song

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
This paper studies the problem of 2D sketch to 3D shape retrieval, but with a focus on democratising the process. We would like this democratisation to happen on two fronts: (i) to remove the need for large-scale specifically sourced 2D sketch and 3D shape datasets, and (ii) to remove restrictions on how well the user needs to sketch and from what viewpoints. The end result is a system that is tra...Show More

Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting

Pinaki Nath Chowdhury;Ayan Kumar Bhunia;Aneeshan Sain;Subhadeep Koley;Tao Xiang;Yi-Zhe Song

Year: 2023 | Conference Paper |

SketchXAI: A First Look at Explainability for Human Sketches

Zhiyu Qu;Yulia Gryaditskaya;Ke Li;Kaiyue Pang;Tao Xiang;Yi-Zhe Song

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a “human-centred” data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by identifying strokes as a unique building block that offers a degree of flexibility in ...Show More

SketchXAI: A First Look at Explainability for Human Sketches

Zhiyu Qu;Yulia Gryaditskaya;Ke Li;Kaiyue Pang;Tao Xiang;Yi-Zhe Song

Year: 2023 | Conference Paper |

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (26)
Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image – just like those shown in Fig. 1(a), all non-cherry-picked. We differ significantly from prior art in that we do not dictate an edgemap-like sketch to start with, but aim to work with abstract free-hand human sketches. In doing so, we essentially democratise the sk...Show More

Picture that Sketch: Photorealistic Image Generation from Abstract Sketches

Subhadeep Koley;Ayan Kumar Bhunia;Aneeshan Sain;Pinaki Nath Chowdhury;Tao Xiang;Yi-Zhe Song

Year: 2023 | Conference Paper |

CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not

Aneeshan Sain;Ayan Kumar Bhunia;Pinaki Nath Chowdhury;Subhadeep Koley;Tao Xiang;Yi-Zhe Song

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (47)
In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best to achieve this synergy, for both the category setting and the fine-grained set...Show More

CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not

Aneeshan Sain;Ayan Kumar Bhunia;Pinaki Nath Chowdhury;Subhadeep Koley;Tao Xiang;Yi-Zhe Song

Year: 2023 | Conference Paper |

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