Recently, the image-wise implicit neural representation of videos, NeRV, has
gained popularity for its promising results and swift speed compared to regular
pixel-wise implicit representations. However, the redundant parameters within
the network structure can cause a large model size when scaling up for
desirable performance. The key reason of this phenomenon is the coupled
formulation of NeRV, which outputs the spatial and temporal information of
video frames directly from the frame index input. In this paper, we propose
E-NeRV, which dramatically expedites NeRV by decomposing the image-wise
implicit neural representation into separate spatial and temporal context.
Under the guidance of this new formulation, our model greatly reduces the
redundant model parameters, while retaining the representation ability. We
experimentally find that our method can improve the performance to a large
extent with fewer parameters, resulting in a more than $8\times$ faster speed
on convergence. Code is available at this https URL

本文提出了一种名为 E-NeRV 的新模型，通过将图像隐式神经表示分解为独立的空间和时间上下文，极大地减少了模型中的冗余参数，同时保留了 NeRV 的表达能力，并使性能得到显著提高。

E-NeRV: 基于解耦空时上下文的神经视频表达加速

E-NeRV: Expedite Neural Video Representation with Disentangled  Spatial-Temporal Context

We propose an end-to-end approach to the natural language object retrieval
task, which localizes an object within an image according to a natural language
description, i.e., referring expression. Previous works divide this problem
into two independent stages: first, compute region proposals from the image
without the exploration of the language description; second, score the object
proposals with regard to the referring expression and choose the top-ranked
proposals. The object proposals are generated independently from the referring
expression, which makes the proposal generation redundant and even irrelevant
to the referred object. In this work, we train an agent with deep reinforcement
learning, which learns to move and reshape a bounding box to localize the
object according to the referring expression. We incorporate both the spatial
and temporal context information into the training procedure. By simultaneously
exploiting local visual information, the spatial and temporal context and the
referring language a priori, the agent selects an appropriate action to take at
each time. A special action is defined to indicate when the agent finds the
referred object, and terminate the procedure. We evaluate our model on various
datasets, and our algorithm significantly outperforms the compared algorithms.
Notably, the accuracy improvement of our method over the recent method GroundeR
and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.

本文介绍一种通过深度强化学习的方式，结合空间和时间情境及自然语言先验知识，移动和重塑边界框以定位描述中的物体，从而实现自然语言目标检索任务。作者实验表明，该方法在多个数据集上均优于现有算法，特别在 ReferItGame 数据集上，该方法相比 GroundeR 和 SCRC 的准确率分别提高了 7.67％和 18.25％。