Neural radiance fields~(NeRF) have recently been applied to render
large-scale scenes. However, their limited model capacity typically results in
blurred rendering results. Existing large-scale NeRFs primarily address this
limitation by partitioning the scene into blocks, which are subsequently
handled by separate sub-NeRFs. These sub-NeRFs, trained from scratch and
processed independently, lead to inconsistencies in geometry and appearance
across the scene. Consequently, the rendering quality fails to exhibit
significant improvement despite the expansion of model capacity. In this work,
we present global-guided focal neural radiance field (GF-NeRF) that achieves
high-fidelity rendering of large-scale scenes. Our proposed GF-NeRF utilizes a
two-stage (Global and Focal) architecture and a global-guided training
strategy. The global stage obtains a continuous representation of the entire
scene while the focal stage decomposes the scene into multiple blocks and
further processes them with distinct sub-encoders. Leveraging this two-stage
architecture, sub-encoders only need fine-tuning based on the global encoder,
thus reducing training complexity in the focal stage while maintaining
scene-wide consistency. Spatial information and error information from the
global stage also benefit the sub-encoders to focus on crucial areas and
effectively capture more details of large-scale scenes. Notably, our approach
does not rely on any prior knowledge about the target scene, attributing
GF-NeRF adaptable to various large-scale scene types, including street-view and
aerial-view scenes. We demonstrate that our method achieves high-fidelity,
natural rendering results on various types of large-scale datasets. Our project
page: this https URL

提出了一种全球引导的焦点神经辐射场 (GF-NeRF)，利用两阶段结构和全球引导的训练策略，在大规模场景中实现高保真度的渲染结果。

全球引导的焦点神经辐射场用于大规模场景渲染

Global-guided Focal Neural Radiance Field for Large-scale Scene  Rendering

Re-ranking draws increased attention on both academics and industries, which
rearranges the ranking list by modeling the mutual influence among items to
better meet users' demands. Many existing re-ranking methods directly take the
initial ranking list as input, and generate the optimal permutation through a
well-designed context-wise model, which brings the evaluation-before-reranking
problem. Meanwhile, evaluating all candidate permutations brings unacceptable
computational costs in practice. Thus, to better balance efficiency and
effectiveness, online systems usually use a two-stage architecture which uses
some heuristic methods such as beam-search to generate a suitable amount of
candidate permutations firstly, which are then fed into the evaluation model to
get the optimal permutation. However, existing methods in both stages can be
improved through the following aspects. As for generation stage, heuristic
methods only use point-wise prediction scores and lack an effective judgment.
As for evaluation stage, most existing context-wise evaluation models only
consider the item context and lack more fine-grained feature context modeling.
This paper presents a novel end-to-end re-ranking framework named PIER to
tackle the above challenges which still follows the two-stage architecture and
contains two mainly modules named FPSM and OCPM. We apply SimHash in FPSM to
select top-K candidates from the full permutation based on user's
permutation-level interest in an efficient way. Then we design a novel
omnidirectional attention mechanism in OCPM to capture the context information
in the permutation. Finally, we jointly train these two modules end-to-end by
introducing a comparative learning loss. Offline experiment results demonstrate
that PIER outperforms baseline models on both public and industrial datasets,
and we have successfully deployed PIER on Meituan food delivery platform.

本研究提出了名为 PIER 的新型端到端重新排序框架，采用两个主要模块 FPSM 和 OCPM，分别基于 SimHash 和全向注意机制提高重新排名效果，实验证明其在公共和工业数据集上均优于基线模型，已成功部署在美团外卖平台。

PIER: 电子商务中基于兴趣的置换级端到端重新排序框架

PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce

The objective of this paper is to perform audio-visual sound source
separation, i.e.~to separate component audios from a mixture based on the
videos of sound sources. Moreover, we aim to pinpoint the source location in
the input video sequence. Recent works have shown impressive audio-visual
separation results when using prior knowledge of the source type (e.g. human
playing instrument) and pre-trained motion detectors (e.g. keypoints or optical
flows). However, at the same time, the models are limited to a certain
application domain. In this paper, we address these limitations and make the
following contributions: i) we propose a two-stage architecture, called
Appearance and Motion network (AMnet), where the stages specialise to
appearance and motion cues, respectively. The entire system is trained in a
self-supervised manner; ii) we introduce an Audio-Motion Embedding (AME)
framework to explicitly represent the motions that related to sound; iii) we
propose an audio-motion transformer architecture for audio and motion feature
fusion; iv) we demonstrate state-of-the-art performance on two challenging
datasets (MUSIC-21 and AVE) despite the fact that we do not use any pre-trained
keypoint detectors or optical flow estimators. Project page:
this https URL

本文提出了一种基于视音频的声源分离方法，其中包括二阶段的体系结构，用于外观和动作特征提取，并引入了音频 - 运动嵌入框架，以明确表示与声音相关的动态物体，该方法无需预训练关键点检测器或光流测量仪，在两个比较具有挑战性的数据集上取得了最先进的性能表现。