mlsanityData to 7 Oct 2026

See Your Heart: Psychological states Interpretation through Visual Creations

arXiv 2302.10276 ↗First posted 11 Feb 2023cs.CV cs.AI cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

In psychoanalysis, generating interpretations to one's psychological state through visual creations is facing significant demands. The two main tasks of existing studies in the field of computer vision, sentiment/emotion classification and affective captioning, can hardly satisfy the requirement of psychological interpreting. To meet the demands for psychoanalysis, we introduce a challenging task, Visual Emotion Interpretation Task (VEIT). VEIT requires AI to generate reasonable interpretations of creator's psychological state through visual creations. To support the task, we present a multimodal dataset termed SpyIn (Sandplay Interpretation Dataset), which is psychological theory supported and professional annotated. Dataset analysis illustrates that SpyIn is not only able to support VEIT, but also more challenging compared with other captioning datasets. Building on SpyIn, we conduct experiments of several image captioning method, and propose a visual-semantic combined model which obtains a SOTA result on SpyIn. The results indicate that VEIT is a more challenging task requiring scene graph information and psychological knowledge. Our work also show a promise for AI to analyze and explain inner world of humanity through visual creations.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 11 Feb 2023.

Builds on

18 of 48 references.

References · 18

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  4. 2015Microsoft COCO Captions: Data Collection and Evaluation Servercited by 1,447
  5. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  6. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  7. 2015Generation and Comprehension of Unambiguous Object Descriptionscited by 897
  8. 2014Show and Tell: A Neural Image Caption Generatorcited by 877
  9. 2016Self-critical Sequence Training for Image Captioningcited by 544
  10. 2019Meshed-Memory Transformer for Image Captioningcited by 343
  11. 2018Graph R-CNN for Scene Graph Generationcited by 296
  12. 2019Attention on Attention for Image Captioningcited by 289
6 more
  1. 2020Learning the Best Pooling Strategy for Visual Semantic Embeddingcited by 136
  2. 2021ArtEmis: Affective Language for Visual Artcited by 103
  3. 2015SentiCap: Generating Image Descriptions with Sentimentscited by 48
  4. 2022It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collectioncited by 36
  5. 2018Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptionscited by 12
  6. 2019Self-critical n-step Training for Image Captioningcited by 12

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.