Recently, one-stage visual grounders attract high attention due to the
comparable accuracy but significantly higher efficiency than two-stage
grounders. However, inter-object relation modeling has not been well studied
for one-stage grounders. Inter-object relationship modeling, though important,
is not necessarily performed among all the objects within the image, as only a
part of them are related to the text query and may confuse the model. We call
these objects "suspected objects". However, exploring relationships among these
suspected objects in the one-stage visual grounding paradigm is non-trivial due
to two core problems: (1) no object proposals are available as the basis on
which to select suspected objects and perform relationship modeling; (2)
compared with those irrelevant to the text query, suspected objects are more
confusing, as they may share similar semantics, be entangled with certain
relationships, etc, and thereby more easily mislead the model's prediction. To
address the above issues, this paper proposes a Suspected Object Graph (SOG)
approach to encourage the correct referred object selection among the suspected
ones in the one-stage visual grounding. Suspected objects are dynamically
selected from a learned activation map as nodes to adapt to the current
discrimination ability of the model during training. Afterward, on top of the
suspected objects, a Keyword-aware Node Representation module (KNR) and an
Exploration by Random Connection strategy (ERC) are concurrently proposed
within the SOG to help the model rethink its initial prediction. Extensive
ablation studies and comparison with state-of-the-art approaches on prevalent
visual grounding benchmarks demonstrate the effectiveness of our proposed
method.

本文提出了一个基于可疑对象图和关键词感知节点表示的一阶视觉定位的方法，用于解决在一阶视觉定位中由于可疑对象的存在导致模型预测错误的问题，并在广泛的基准测试中展示了其有效性。

怀疑对象的重要性：重新思考单阶段视觉定位模型的预测

Suspected Object Matters: Rethinking Model's Prediction for One-stage Visual Grounding

One-stage object detection is commonly implemented by optimizing two
sub-tasks: object classification and localization, using heads with two
parallel branches, which might lead to a certain level of spatial misalignment
in predictions between the two tasks. In this work, we propose a Task-aligned
One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a
learning-based manner. First, we design a novel Task-aligned Head (T-Head)
which offers a better balance between learning task-interactive and
task-specific features, as well as a greater flexibility to learn the alignment
via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL)
to explicitly pull closer (or even unify) the optimal anchors for the two tasks
during training via a designed sample assignment scheme and a task-aligned
loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a
51.1 AP at single-model single-scale testing. This surpasses the recent
one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP),
and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also
demonstrate the effectiveness of TOOD for better aligning the tasks of object
classification and localization. Code is available at
this https URL

本文提出一种 Task-aligned One-stage Object Detection（TOOD）方法，通过设计一种 Task-aligned Head（T-Head）和 Task Alignment Learning（TAL），在学习过程中显示地对齐对象分类和定位这两个任务，实验结果表明，TOOD 方法比最近的一阶段检测器在准确率方面都要优秀。

TOOD：任务对齐的一阶段目标检测

TOOD: Task-aligned One-stage Object Detection

With the knowledge of action moments (i.e., trimmed video clips that each
contains an action instance), humans could routinely localize an action
temporally in an untrimmed video. Nevertheless, most practical methods still
require all training videos to be labeled with temporal annotations (action
category and temporal boundary) and develop the models in a fully-supervised
manner, despite expensive labeling efforts and inapplicable to new categories.
In this paper, we introduce a new design of transfer learning type to learn
action localization for a large set of action categories, but only on action
moments from the categories of interest and temporal annotations of untrimmed
videos from a small set of action classes. Specifically, we present Action
Herald Networks (AherNet) that integrate such design into an one-stage action
localization framework. Technically, a weight transfer function is uniquely
devised to build the transformation between classification of action moments or
foreground video segments and action localization in synthetic contextual
moments or untrimmed videos. The context of each moment is learnt through the
adversarial mechanism to differentiate the generated features from those of
background in untrimmed videos. Extensive experiments are conducted on the
learning both across the splits of ActivityNet v1.3 and from THUMOS14 to
ActivityNet v1.3. Our AherNet demonstrates the superiority even comparing to
most fully-supervised action localization methods. More remarkably, we train
AherNet to localize actions from 600 categories on the leverage of action
moments in Kinetics-600 and temporal annotations from 200 classes in
ActivityNet v1.3. Source code and data are available at
https://github.com/FuchenUSTC/AherNet.

本文提出了一种新型的转移学习方法来学习大量的动作类别的动作定位，但只通过感兴趣的类别的动作片段和少量的动作类别的时间注释，在一个阶段动作定位框架中集成了这种设计。通过对 ActivityNet v1.3 和 THUMOS14 的实验，AherNet 比大多数完全监督的动作定位方法表现优越。