ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2001.09671
  4. Cited By
Explaining with Counter Visual Attributes and Examples

Explaining with Counter Visual Attributes and Examples

International Conference on Multimedia Retrieval (ICMR), 2020
27 January 2020
Sadaf Gulshad
A. Smeulders
    XAIFAttAAML
ArXiv (abs)PDFHTML

Papers citing "Explaining with Counter Visual Attributes and Examples"

5 / 5 papers shown
Title
Multimodal Explainable Artificial Intelligence: A Comprehensive Review
  of Methodological Advances and Future Research Directions
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research DirectionsIEEE Access (IEEE Access), 2023
N. Rodis
Christos Sardianos
Panagiotis I. Radoglou-Grammatikis
Panagiotis G. Sarigiannidis
Iraklis Varlamis
Georgios Th. Papadopoulos
185
34
0
09 Jun 2023
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with
  Multi-Annotators
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators
Yuan Cheng
Zu-Yun Shiau
Fu-En Yang
Yu-Chiang Frank Wang
130
6
0
19 Feb 2023
Hierarchical Explanations for Video Action Recognition
Hierarchical Explanations for Video Action Recognition
Sadaf Gulshad
Teng Long
Nanne van Noord
FAtt
260
12
0
01 Jan 2023
Concept Evolution in Deep Learning Training: A Unified Interpretation
  Framework and Discoveries
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and DiscoveriesInternational Conference on Information and Knowledge Management (CIKM), 2022
Haekyu Park
Seongmin Lee
Benjamin Hoover
Austin P. Wright
Omar Shaikh
Rahul Duggal
Nilaksh Das
Kevin Wenliang Li
Judy Hoffman
Duen Horng Chau
159
3
0
30 Mar 2022
A Review on Explainability in Multimodal Deep Neural Nets
A Review on Explainability in Multimodal Deep Neural NetsIEEE Access (IEEE Access), 2021
Gargi Joshi
Rahee Walambe
K. Kotecha
174
164
0
17 May 2021
1