SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text
Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley, Tao Xiang, Yi-Zhe Song
Abstract
In this paper, we extend scene understanding to include that of human sketch. The result is a complete trilogy of scene representation from three diverse and complementary modalities -sketch, photo, and text. Instead of learning a rigid three-way embedding and be done with it, we focus on learning a flexible joint embedding that fully supports the "optionality" that this complementarity brings. Our embedding supports optionality on two axes: (i) optionality across modalities -use any combination of modalities as query for downstream tasks like retrieval, (ii) optionality across tasks -simultaneously utilising the embedding for either discriminative (e.g., retrieval) or generative tasks (e.g., captioning). This provides flexibility to end-users by exploiting the best of each modality, therefore serving the very purpose behind our proposal of a trilogy in the first place. First, a combination of information-bottleneck and conditional invertible neural networks disentangle the modalityspecific component from modality-agnostic in sketch, photo, and text. Second, the modality-agnostic instances from sketch, photo, and text are synergised using a modified cross-attention. Once learned, we show our embedding can accommodate a multi-facet of scene-related tasks, including those enabled for the first time by the inclusion of sketch, all without any task-specific modifications. Project
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Cited by top-tier papers2
- Composite Sketch+Text Queries for Retrieving Objects with Elusive Names and Complex InteractionsPrajwal Gatti, Kshitij Parikh, Dhriti Prasanna Paul, Manish Gupta et al.AAAI 2024 · 6 citations
- Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's EducationYanhao Jia, Xinyi Wu, Li Hao, Qinglin Zhang et al.ACL 2025
Builds on28
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Training data-efficient image transformers & distillation through attentionHugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa et al.ICML 2021 · 8,974 citations
- Scaling Up Visual and Vision-Language Representation Learning With Noisy Text SupervisionChao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen et al.ICML 2021 · 5,401 citations
- ViLT: Vision-and-Language Transformer Without Convolution or Region SupervisionWonjae Kim, Bokyung Son, Ildoo KimICML 2021 · 2,258 citations
- nocaps: novel object captioning at scaleHarsh Agrawal, Peter Anderson, Karan Desai, Yufei Wang et al.ICCV 2019 · 631 citations
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