The task of language-guided video temporal grounding is to localize the
particular video clip corresponding to a query sentence in an untrimmed video.
Though progress has been made continuously in this field, some issues still
need to be resolved. First, most of the existing methods rely on the
combination of multiple complicated modules to solve the task. Second, due to
the semantic gaps between the two different modalities, aligning the
information at different granularities (local and global) between the video and
the language is significant, which is less addressed. Last, previous works do
not consider the inevitable annotation bias due to the ambiguities of action
boundaries. To address these limitations, we propose a simple two-branch
Cross-Modality Attention (CMA) module with intuitive structure design, which
alternatively modulates two modalities for better matching the information both
locally and globally. In addition, we introduce a new task-specific regression
loss function, which improves the temporal grounding accuracy by alleviating
the impact of annotation bias. We conduct extensive experiments to validate our
method, and the results show that just with this simple model, it can
outperform the state of the arts on both Charades-STA and ActivityNet Captions
datasets.

本文主要研究了利用语言引导定位视频中的相关片段的问题，提出一种简单直观的跨模态注意力模块 (CMA) 和针对此任务的新回归损失函数来提高定位精度，并在 Charades-STA 和 ActivityNet Captions 数据集上超越了目前最先进的方法。

一种简单而有效的视频时间对齐方法，具有交叉模态注意力

A Simple Yet Effective Method for Video Temporal Grounding with  Cross-Modality Attention

Predicting a scene graph that captures visual entities and their interactions
in an image has been considered a crucial step towards full scene
comprehension. Recent scene graph generation (SGG) models have shown their
capability of capturing the most frequent relations among visual entities.
However, the state-of-the-art results are still far from satisfactory, e.g.
models can obtain 31% in overall recall R@100, whereas the likewise important
mean class-wise recall mR@100 is only around 8% on Visual Genome (VG). The
discrepancy between R and mR results urges to shift the focus from pursuing a
high R to a high mR with a still competitive R. We suspect that the observed
discrepancy stems from both the annotation bias and sparse annotations in VG,
in which many visual entity pairs are either not annotated at all or only with
a single relation when multiple ones could be valid. To address this particular
issue, we propose a novel SGG training scheme that capitalizes on self-learned
knowledge. It involves two relation classifiers, one offering a less biased
setting for the other to base on. The proposed scheme can be applied to most of
the existing SGG models and is straightforward to implement. We observe
significant relative improvements in mR (between +6.6% and +20.4%) and
competitive or better R (between -2.4% and 0.3%) across all standard SGG tasks.

提出了一种基于自学知识的场景图生成模型训练方案，使用两个关系分类器减少标注偏差和稀疏标注造成的影响，并在多个标准场景图生成任务中观察到了显著的相对改进。

减少偏差的模型生成场景图

Tackling the Unannotated: Scene Graph Generation with Bias-Reduced  Models

A critical issue in pedestrian detection is to detect small-scale objects
that will introduce feeble contrast and motion blur in images and videos, which
in our opinion should partially resort to deep-rooted annotation bias.
Motivated by this, we propose a novel method integrated with somatic
topological line localization (TLL) and temporal feature aggregation for
detecting multi-scale pedestrians, which works particularly well with
small-scale pedestrians that are relatively far from the camera. Moreover, a
post-processing scheme based on Markov Random Field (MRF) is introduced to
eliminate ambiguities in occlusion cases. Applying with these methodologies
comprehensively, we achieve best detection performance on Caltech benchmark and
improve performance of small-scale objects significantly (miss rate decreases
from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on
CityPersons dataset and show the existence of annotation bias in KITTI dataset.

本文提出一种基于身形拓扑线定位和时间特征聚合的新型方法，针对行人检测中小尺度目标识别难题，提出了一种综合性的解决方案，包括 MRF 后处理方案，能有效减少漏检率，并在 Caltech 和 CityPersons 数据集上都取得了有竞争力的性能。同时也探测出了 KITTI 数据集中的注释偏差存在。