415 citations · 967 across the 32 of their papers we have counts for
34 papers
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…
PACO: Parts and Attributes of Common Objects
Vignesh Ramanathan, Anmol Kalia, Vladan Petrovic +11
Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets whic…
ObjectStitch: Generative Object Compositing
Yizhi Song, Zhifei Zhang, Zhe Lin +5
Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow ge…
Towards 3D VR-Sketch to 3D Shape Retrieval
Ling Luo, Yulia Gryaditskaya, Yongxin Yang +2
Growing free online 3D shapes collections dictated research on 3D retrieval. Active debate has however been had on (i) what the best input modality is to trigger retrieval, and (ii…
SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling
Chenjian Gao, Qian Yu, Lu Sheng +2
Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap between a sparse, irregular sketch and a regular, dense 3D shape. Existing works…
Making a Bird AI Expert Work for You and Me
Dongliang Chang, Kaiyue Pang, Ruoyi Du +3
As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of "Whip-poor-will" or "Mallard" probably does not make much sense. This however…
Fine-Grained Image Analysis with Deep Learning: A Survey
Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha +5
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The…
Your "Flamingo" is My "Bird": Fine-Grained, or Not
Dongliang Chang, Kaiyue Pang, Yixiao Zheng +3
Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former,…
Deep Sketch-Based Modeling: Tips and Tricks
Yue Zhong, Yulia Gryaditskaya, Honggang Zhang +1
Deep image-based modeling received lots of attention in recent years, yet the parallel problem of sketch-based modeling has only been briefly studied, often as a potential applicat…
Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval
Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang +2
Sketch as an image search query is an ideal alternative to text in capturing the fine-grained visual details. Prior successes on fine-grained sketch-based image retrieval (FG-SBIR)…
BézierSketch: A generative model for scalable vector sketches
Ayan Das, Yongxin Yang, Timothy Hospedales +2
The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing proces…
On Learning Semantic Representations for Million-Scale Free-Hand Sketches
Peng Xu, Yongye Huang, Tongtong Yuan +4
In this paper, we study learning semantic representations for million-scale free-hand sketches. This is highly challenging due to the domain-unique traits of sketches, e.g., divers…
Sequential Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
In this paper we propose a sequential learning framework for Domain Generalization (DG), the problem of training a model that is robust to domain shift by design. Various DG approa…
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
Dongliang Chang, Aneeshan Sain, Zhanyu Ma +2
Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain. Among its many variants, open set domain ada…
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
Ruoyi Du, Dongliang Chang, Ayan Kumar Bhunia +4
Fine-grained visual classification (FGVC) is much more challenging than traditional classification tasks due to the inherently subtle intra-class object variations. Recent works ma…
Fine-Grained Instance-Level Sketch-Based Video Retrieval
Peng Xu, Kun Liu, Tao Xiang +4
Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level ske…
Deep Self-Supervised Representation Learning for Free-Hand Sketch
Peng Xu, Zeyu Song, Qiyue Yin +2
In this paper, we tackle for the first time, the problem of self-supervised representation learning for free-hand sketches. This importantly addresses a common problem faced by the…
Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales +2
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is how…
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Dongliang Chang, Yifeng Ding, Jiyang Xie +6
Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex netw…
SketchDesc: Learning Local Sketch Descriptors for Multi-view Correspondence
Deng Yu, Lei Li, Youyi Zheng +4
In this paper, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as ou…
Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
Jianjun Lei, Yuxin Song, Bo Peng +3
Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images i…
Goal-Driven Sequential Data Abstraction
Umar Riaz Muhammad, Yongxin Yang, Timothy M. Hospedales +2
Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine…
Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval
Sounak Dey, Pau Riba, Anjan Dutta +2
In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen c…
Episodic Training for Domain Generalization
Da Li, Jianshu Zhang, Yongxin Yang +3
Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training…