Object-centric learning aims to break down complex visual scenes into more
manageable object representations, enhancing the understanding and reasoning
abilities of machine learning systems toward the physical world. Recently,
slot-based video models have demonstrated remarkable proficiency in segmenting
and tracking objects, but they overlook the importance of the effective
reasoning module. In the real world, reasoning and predictive abilities play a
crucial role in human perception and object tracking; in particular, these
abilities are closely related to human intuitive physics. Inspired by this, we
designed a novel reasoning module called the Slot-based Time-Space Transformer
with Memory buffer (STATM) to enhance the model's perception ability in complex
scenes. The memory buffer primarily serves as storage for slot information from
upstream modules, the Slot-based Time-Space Transformer makes predictions
through slot-based spatiotemporal attention computations and fusion. Our
experiment results on various datasets show that STATM can significantly
enhance object-centric learning capabilities of slot-based video models.

通过设计一种名为 STATM 的新型推理模块，可以显著增强基于插槽的视频模型的物体中心学习能力。

视频的推理增强的物体中心学习

Reasoning-Enhanced Object-Centric Learning for Videos

In this article, we tackle the math word problem, namely, automatically
answering a mathematical problem according to its textual description. Although
recent methods have demonstrated their promising results, most of these methods
are based on template-based generation scheme which results in limited
generalization capability. To this end, we propose a novel human-like
analogical learning method in a recall and learn manner. Our proposed framework
is composed of modules of memory, representation, analogy, and reasoning, which
are designed to make a new exercise by referring to the exercises learned in
the past. Specifically, given a math word problem, the model first retrieves
similar questions by a memory module and then encodes the unsolved problem and
each retrieved question using a representation module. Moreover, to solve the
problem in a way of analogy, an analogy module and a reasoning module with a
copy mechanism are proposed to model the interrelationship between the problem
and each retrieved question. Extensive experiments on two well-known datasets
show the superiority of our proposed algorithm as compared to other
state-of-the-art competitors from both overall performance comparison and
micro-scope studies.

本文介绍了一种人类模拟学习的方法，利用记忆模块、表征模块、类比模块和推理模块来解决数学文字题，实验结果表明该算法比其他现有算法表现更好。

Recall and Learn: 数学问题记忆增强求解器

Recall and Learn: A Memory-augmented Solver for Math Word Problems

Visual Question Answering (VQA) has attracted attention from both computer
vision and natural language processing communities. Most existing approaches
adopt the pipeline of representing an image via pre-trained CNNs, and then
using the uninterpretable CNN features in conjunction with the question to
predict the answer. Although such end-to-end models might report promising
performance, they rarely provide any insight, apart from the answer, into the
VQA process. In this work, we propose to break up the end-to-end VQA into two
steps: explaining and reasoning, in an attempt towards a more explainable VQA
by shedding light on the intermediate results between these two steps. To that
end, we first extract attributes and generate descriptions as explanations for
an image using pre-trained attribute detectors and image captioning models,
respectively. Next, a reasoning module utilizes these explanations in place of
the image to infer an answer to the question. The advantages of such a
breakdown include: (1) the attributes and captions can reflect what the system
extracts from the image, thus can provide some explanations for the predicted
answer; (2) these intermediate results can help us identify the inabilities of
both the image understanding part and the answer inference part when the
predicted answer is wrong. We conduct extensive experiments on a popular VQA
dataset and dissect all results according to several measurements of the
explanation quality. Our system achieves comparable performance with the
state-of-the-art, yet with added benefits of explainability and the inherent
ability to further improve with higher quality explanations.

本研究提出将端到端的 VQA 分解为解释和推理两步，使用预训练的属性检测器和图像字幕模型提取图像属性和生成图像描述，然后使用推理模块将这些解释代替图像推断问题的答案。通过对热门 VQA 数据集进行实验，我们证明了该系统具备解释性和进一步提高解释质量的内在能力。

Tell-and-Answer: 基于属性和字幕的可解释视觉问答

Tell-and-Answer: Towards Explainable Visual Question Answering using  Attributes and Captions

This paper presents a novel neural model - Dynamic Fusion Network (DFN), for
machine reading comprehension (MRC). DFNs differ from most state-of-the-art
models in their use of a dynamic multi-strategy attention process, in which
passages, questions and answer candidates are jointly fused into attention
vectors, along with a dynamic multi-step reasoning module for generating
answers. With the use of reinforcement learning, for each input sample that
consists of a question, a passage and a list of candidate answers, an instance
of DFN with a sample-specific network architecture can be dynamically
constructed by determining what attention strategy to apply and how many
reasoning steps to take. Experiments show that DFNs achieve the best result
reported on RACE, a challenging MRC dataset that contains real human reading
questions in a wide variety of types. A detailed empirical analysis also
demonstrates that DFNs can produce attention vectors that summarize information
from questions, passages and answer candidates more effectively than other
popular MRC models.

本文提出了一种新型神经模型 - Dynamic Fusion Network （DFN），用于机器阅读理解，并使用动态多策略注意过程进行多个步骤的推理来生成答案，发现 DFNs 可以比其他流行的机器阅读理解模型更有效地生成从问题、段落和候选答案中总结信息的注意力向量，并取得了 RACE 数据集中的最佳结果。