In this paper, we introduce DiffusionMat, a novel image matting framework
that employs a diffusion model for the transition from coarse to refined alpha
mattes. Diverging from conventional methods that utilize trimaps merely as
loose guidance for alpha matte prediction, our approach treats image matting as
a sequential refinement learning process. This process begins with the addition
of noise to trimaps and iteratively denoises them using a pre-trained diffusion
model, which incrementally guides the prediction towards a clean alpha matte.
The key innovation of our framework is a correction module that adjusts the
output at each denoising step, ensuring that the final result is consistent
with the input image's structures. We also introduce the Alpha Reliability
Propagation, a novel technique designed to maximize the utility of available
guidance by selectively enhancing the trimap regions with confident alpha
information, thus simplifying the correction task. To train the correction
module, we devise specialized loss functions that target the accuracy of the
alpha matte's edges and the consistency of its opaque and transparent regions.
We evaluate our model across several image matting benchmarks, and the results
indicate that DiffusionMat consistently outperforms existing methods. Project
page at~\url{this https URL

本文介绍了一种名为 DiffusionMat 的新型图像抠像框架，它采用扩散模型从粗糙到精细的透明度图过渡。通过将噪声添加到阈值图并使用预训练的扩散模型迭代去噪，我们的方法将图像抠像视为一种顺序细化学习过程，并通过校正模块确保最终输出与输入图像的结构一致。此外，我们引入了 Alpha 可靠性传播技术，该技术通过选择性增强自信透明度信息的阈值区域来简化校正任务。通过使用专门针对透明度图边缘准确性和不透明与透明区域一致性的损失函数训练校正模块，我们评估了我们的模型，并结果表明 DiffusionMat 始终优于现有方法。

DiffusionMat：Alpha Matting 作为顺序精化学习

DiffusionMat: Alpha Matting as Sequential Refinement Learning

Most dense retrieval models contain an implicit assumption: the training
query-document pairs are exactly matched. Since it is expensive to annotate the
corpus manually, training pairs in real-world applications are usually
collected automatically, which inevitably introduces mismatched-pair noise. In
this paper, we explore an interesting and challenging problem in dense
retrieval, how to train an effective model with mismatched-pair noise. To solve
this problem, we propose a novel approach called Noisy Pair Corrector (NPC),
which consists of a detection module and a correction module. The detection
module estimates noise pairs by calculating the perplexity between annotated
positive and easy negative documents. The correction module utilizes an
exponential moving average (EMA) model to provide a soft supervised signal,
aiding in mitigating the effects of noise. We conduct experiments on
text-retrieval benchmarks Natural Question and TriviaQA, code-search benchmarks
StaQC and SO-DS. Experimental results show that NPC achieves excellent
performance in handling both synthetic and realistic noise.

本文介绍了一种名为 Noisy Pair Corrector (NPC) 的新方法，该方法包含一个检测模块和一个校正模块，用于训练具有不匹配对噪声的有效模型，并在实验中展示了其在处理合成和实际噪声方面的出色性能。

密集检索的噪声对矫正器

Noisy Pair Corrector for Dense Retrieval

We present an approach to learn an object-centric forward model, and show
that this allows us to plan for sequences of actions to achieve distant desired
goals. We propose to model a scene as a collection of objects, each with an
explicit spatial location and implicit visual feature, and learn to model the
effects of actions using random interaction data. Our model allows capturing
the robot-object and object-object interactions, and leads to more
sample-efficient and accurate predictions. We show that this learned model can
be leveraged to search for action sequences that lead to desired goal
configurations, and that in conjunction with a learned correction module, this
allows for robust closed loop execution. We present experiments both in
simulation and the real world, and show that our approach improves over
alternate implicit or pixel-space forward models. Please see our project page
(this https URL) for result videos.

该论文提出了一种基于物体的前向模型学习方法，用于规划动作序列以实现远程期望目标，并通过随机交互数据来学习行为效果。实验结果表明，该方法具有更高的采样效率和准确性，可以优化闭环执行。