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Found 8 papers, 2 papers with code
StyleBooth: Image Style Editing with Multimodal Instruction
We integrate encoded textual instruction and image exemplar as a unified condition for diffusion model, enabling the editing of original image following multimodal instructions.
Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance
The process involves (i) Locate: concatenating the noise with masked scene image to achieve precise regional editing, (ii) Assign: employing decoupled cross-attention mechanism to accommodate multi-modal guidance, and (iii) Refine: using a novel RefineNet to supplement subject details.
Res-Attn : An Enhanced Res-Tuning Approach with Lightweight Attention Mechanism
Res-Tuning introduces a flexible and efficient paradigm for model tuning, showing that tuners decoupled from the backbone network can achieve performance comparable to traditional methods.
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis.
Rethinking Efficient Tuning Methods from a Unified Perspective
The U-Tuning framework can simultaneously encompass existing methods and derive new approaches for parameter-efficient transfer learning, which prove to achieve on-par or better performances on CIFAR-100 and FGVC datasets when compared with existing PETL methods.
NGC: A Unified Framework for Learning with Open-World Noisy Data
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance.
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Image Classification
on mini WebVision 1.0
Pyramid Person Matching Network for Person Re-identification
In this work, we present a deep convolutional pyramid person matching network (PPMN) with specially designed Pyramid Matching Module to address the problem of person re-identification.
Multi-Channel Pyramid Person Matching Network for Person Re-Identification
In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations.
