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Found 5 papers, 2 papers with code
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
In DeiT-LT, we introduce an efficient and effective way of distillation from CNN via distillation DIST token by using out-of-distribution images and re-weighting the distillation loss to enhance focus on tail classes.
Ranked #1 on
Image Classification
on iNaturalist
(Overall metric)
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain.
Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
The proposed approach achieves state-of-the-art results on the standard Domain Generalization benchmarks in a black-box teacher setting as well as a white-box setting where the weights of the VLM are accessible.
Ranked #2 on
Domain Generalization
on DomainNet
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
We are the first to utilize vision transformers for domain adaptation in a privacy-oriented source-free setting, and our approach achieves state-of-the-art performance on single-source, multi-source, and multi-target benchmarks
Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors
We then evaluate the influence of our proposed concepts at multiple layers of the Bi-directional Contextual LSTM (BC-LSTM) network to show that the reasoning process of neural networks for emotion recognition can be represented using human-understandable concepts.
