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Found 8 papers, 2 papers with code
One-dimensional Adapter to Rule Them All: Concepts Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
Open-Vocabulary Object Detection With an Open Corpus
For the classification task, we introduce an open corpus classifier by reconstructing original classifier with similar words in text space.
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition
Finally, we propose a weakly supervised object localization-based approach to extract multi-scale local features, to form a multi-view pipeline.
Multi-Label Image Recognition
Weakly-Supervised Object Localization
DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
In multimodal tasks, we find that the importance of text and image modal information is different for different input cases, and for this motivation, we propose a high-performance and highly general Dual-Router Dynamic Framework (DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit.
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
We propose the recurrent attention multi-scale transformer (RAMS-Trans), which uses the transformer's self-attention to recursively learn discriminative region attention in a multi-scale manner.
Ranked #9 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Image Recognition
The Open Brands Dataset: Unified brand detection and recognition at scale
To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations.
Heuristic Domain Adaptation
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem.
