In natural vision, feedback connections support versatile visual inference
capabilities such as making sense of the occluded or noisy bottom-up sensory
information or mediating pure top-down processes such as imagination. However,
the mechanisms by which the feedback pathway learns to give rise to these
capabilities flexibly are not clear. We propose that top-down effects emerge
through alignment between feedforward and feedback pathways, each optimizing
its own objectives. To achieve this co-optimization, we introduce
Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages
feedback and feedforward pathways as mutual credit assignment computational
graphs, enabling alignment. In our study, we demonstrate the effectiveness of
FFA in co-optimizing classification and reconstruction tasks on widely used
MNIST and CIFAR10 datasets. Notably, the alignment mechanism in FFA endows
feedback connections with emergent visual inference functions, including
denoising, resolving occlusions, hallucination, and imagination. Moreover, FFA
offers bio-plausibility compared to traditional backpropagation (BP) methods in
implementation. By repurposing the computational graph of credit assignment
into a goal-driven feedback pathway, FFA alleviates weight transport problems
encountered in BP, enhancing the bio-plausibility of the learning algorithm.
Our study presents FFA as a promising proof-of-concept for the mechanisms
underlying how feedback connections in the visual cortex support flexible
visual functions. This work also contributes to the broader field of visual
inference underlying perceptual phenomena and has implications for developing
more biologically inspired learning algorithms.

通过反馈与前馈路径的关联对齐机制，提出了一种学习算法 Feedback-Feedforward Alignment (FFA)，通过共同优化分类和重建任务，使得反馈连接具有去噪、解决遮挡、幻觉和想象等视觉推理功能，并提供了比传统的反向传播方法更接近生物合理性的实现。这项研究为解释视觉皮层中反馈连接支持灵活视觉功能的机制提供了有前景的概念证明，也为感知现象下的视觉推理领域做出了贡献，对于开发更具生物启发的学习算法具有重要意义。

脑样柔性视觉推理：通过利用反馈前馈对齐

Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward  Alignment

People often refer to entities in an image in terms of their relationships
with other entities. For example, "the black cat sitting under the table"
refers to both a "black cat" entity and its relationship with another "table"
entity. Understanding these relationships is essential for interpreting and
grounding such natural language expressions. Most prior work focuses on either
grounding entire referential expressions holistically to one region, or
localizing relationships based on a fixed set of categories. In this paper we
instead present a modular deep architecture capable of analyzing referential
expressions into their component parts, identifying entities and relationships
mentioned in the input expression and grounding them all in the scene. We call
this approach Compositional Modular Networks (CMNs): a novel architecture that
learns linguistic analysis and visual inference end-to-end. Our approach is
built around two types of neural modules that inspect local regions and
pairwise interactions between regions. We evaluate CMNs on multiple referential
expression datasets, outperforming state-of-the-art approaches on all tasks.

本文提出了一种名为组合模块网络（Compositional Modular Networks，CMNs）的深度架构，该架构能够分析指称表达式的组成部分，识别输入表达式中提及的实体和关系，并将它们统一在现场中，从而实现对这些自然语言表达式的理解和建立联系。 通过在多个参考表达式数据集上进行评估，我们证明了 CMNs 技术在所有任务上优于现有技术。

使用组合模块化网络建模指代表达式中的关系

Modeling Relationships in Referential Expressions with Compositional  Modular Networks

To produce images that are suitable for display, tone-mapping is widely used
in digital cameras to map linear color measurements into narrow gamuts with
limited dynamic range. This introduces non-linear distortion that must be
undone, through a radiometric calibration process, before computer vision
systems can analyze such photographs radiometrically. This paper considers the
inherent uncertainty of undoing the effects of tone-mapping. We observe that
this uncertainty varies substantially across color space, making some pixels
more reliable than others. We introduce a model for this uncertainty and a
method for fitting it to a given camera or imaging pipeline. Once fit, the
model provides for each pixel in a tone-mapped digital photograph a probability
distribution over linear scene colors that could have induced it. We
demonstrate how these distributions can be useful for visual inference by
incorporating them into estimation algorithms for a representative set of
vision tasks.

本文研究了色调映射的效应对于数字照片的非线性失真以及该失真造成的校准不确定性。我们提出并实现了一种模型和方法来解决这个问题，并用不同的视觉任务来展示了这种方法的有效性。