Sketch Me That Shoe

By Qian Yu, Feng Liu, Yi-Zhe Song, Tao A. Xiang, Timothy M. Hospedales, Chen Change Loy. 2016

481 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (2022, methodology): “For learning the joint embedding space, category-level SBIR typically employs either CNN [11, 12], RNN [65], or Transformer [41] based Siamese networks, accompanied by a triplet-ranking objective [68].”
Deeper, Broader and Artier Domain Generalization (2017, neutral): “A more compelling motivation is domains where the total available images is fundamentally constrained, such as for particular styles of art [5, 29], and sketches [33, 6, 34, 26].”
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval (2018, background): “Few medium-scale sketch datasets exist [3, 32, 24, 20].”
Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches (2020, background): “Some human-drawn sketch datasets have also been collected [24, 37] to train sketch-based models [3, 17].”
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis (2018, background): “[60] introduced a new dataset with paired sketches and images, but there are only two categories – shoes and chairs.”
Cross-Domain Adversarial Feature Learning for Sketch Re-identification (2018, methodology): “5, we compare our sketch Re-ID dataset with other two sketch datasets including CUFSF dataset [32] and QMUL-shoe dataset [29].”
Sketchformer: Transformer-Based Representation for Sketched Structure (2020, methodology): “Sketch-A-Net was a seminal work for sketch clas-siﬁcation that employed a CNN with large convolutional kernels to accommodate the sparsity of stroke pixels [34].”
Creative Sketch Generation (2020, background): “, 2018), sketch-based image retrieval (Yu et al., 2016; Liu et al., 2017; Ribeiro et al., 2020) and generation (Gao et al.”
Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking (2020, methodology): “A large number of existing SBIR methods adopt this strategy to overcome the domain gap [9], [11]–[14], [19], [23], [27], [31], [34], [36]–[39], [42], [43].”
StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval (2021, methodology): “For FG-SBIR, two publicly available datasets, QMUL-Chair-V2 and QMUL-Shoe-V2 [60, 44] are used.”
Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval (2017, methodology): “Existing pairwise verification or triplet ranking losses [46, 31] are sensitive to misalignment.”
Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting (2021, background): “Sketch Representation Learning: Learning good sketch representations benefits a variety of sketch-specific problems like classification [64], retrieval [62], scene understanding [38], sketch based image retrieval [7, 45, 46, 14, 16, 4, 53, 52], generative sketch modelling [57, 22, 6] etc.”
Sketch3T: Test-Time Training for Zero-Shot SBIR (2022, methodology): “(iii) MetaLearning Baselines (Meta): Meta-SN-ZS simply employs vanilla MAML [18] on top of a simple Siamese-network following [66], trained via triplet loss in both inner and outer loops, in a zero-shot retrieval framework.”
Significance of Softmax-Based Features in Comparison to Distance Metric Learning-Based Features (2017, background): “For cross-domain tasks, such as sketches to photos [30], [31] or aerial views to ground views [32], DML is effective.”
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval (2019, background): “opment of deep learning, researchers have introduced deep neural networks into this field [22, 45, 21, 43, 37, 33, 30, 44, 42, 39].”
Doodle to Search: Practical Zero-Shot Sketch-Based Image Retrieval (2019, methodology): “Two more finegrained SBIR datasets with paired sketches and images are shoe and chair datasets which were proposed in [34].”
Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval (2017, methodology): “We show that our model outperforms both state-of-the-art models [19, 27] significantly.”
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval (2021, methodology): “Recent FG-SBIR works [48, 26, 5, 28] predominately Photo to Sketch Generation Model”
Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval (2020, methodology): “We evaluate our model against three groups of competitors: (i) Evaluation against existing state-of-the-arts (SoA): Triplet-SN [50] uses Sketch-a-Net [51] as a baseline feature extractor trained using triplet ranking loss.”