Image retrieval targets to find images from a database that are visually
similar to the query image. Two-stage methods following retrieve-and-rerank
paradigm have achieved excellent performance, but their separate local and
global modules are inefficient to real-world applications. To better trade-off
retrieval efficiency and accuracy, some approaches fuse global and local
feature into a joint representation to perform single-stage image retrieval.
However, they are still challenging due to various situations to tackle,
$e.g.$, background, occlusion and viewpoint. In this work, we design a
Coarse-to-Fine framework to learn Compact Discriminative representation (CFCD)
for end-to-end single-stage image retrieval-requiring only image-level labels.
Specifically, we first design a novel adaptive softmax-based loss which
dynamically tunes its scale and margin within each mini-batch and increases
them progressively to strengthen supervision during training and intra-class
compactness. Furthermore, we propose a mechanism which attentively selects
prominent local descriptors and infuse fine-grained semantic relations into the
global representation by a hard negative sampling strategy to optimize
inter-class distinctiveness at a global scale. Extensive experimental results
have demonstrated the effectiveness of our method, which achieves
state-of-the-art single-stage image retrieval performance on benchmarks such as
Revisited Oxford and Revisited Paris. Code is available at
this https URL

本研究设计了一个用于单阶段图像检索的紧凑鉴别性表示学习框架，只需要图像级别的标签，通过动态调整损失尺度和边界、选择重要的局部描述符并注入细粒度语义关系，实现了全局尺度上的类间区分度优化，取得了在 Revisited Oxford 和 Revisited Paris 等基准测试中的最新单阶段图像检索性能。

粗到细：学习单阶段图像检索的紧凑判别表示

Coarse-to-Fine: Learning Compact Discriminative Representation for  Single-Stage Image Retrieval

Two-stage methods have dominated Human-Object Interaction (HOI) detection for
several years. Recently, one-stage HOI detection methods have become popular.
In this paper, we aim to explore the essential pros and cons of two-stage and
one-stage methods. With this as the goal, we find that conventional two-stage
methods mainly suffer from positioning positive interactive human-object pairs,
while one-stage methods are challenging to make an appropriate trade-off on
multi-task learning, i.e., object detection, and interaction classification.
Therefore, a core problem is how to take the essence and discard the dregs from
the conventional two types of methods. To this end, we propose a novel
one-stage framework with disentangling human-object detection and interaction
classification in a cascade manner. In detail, we first design a human-object
pair generator based on a state-of-the-art one-stage HOI detector by removing
the interaction classification module or head and then design a relatively
isolated interaction classifier to classify each human-object pair. Two cascade
decoders in our proposed framework can focus on one specific task, detection or
interaction classification. In terms of the specific implementation, we adopt a
transformer-based HOI detector as our base model. The newly introduced
disentangling paradigm outperforms existing methods by a large margin, with a
significant relative mAP gain of 9.32% on HICO-Det. The source codes are
available at this https URL

本文旨在探讨基于两阶段和单阶段方式的人 - 物交互检测方法各自的优点和缺点，并提出一种新的单阶段框架，该框架采用分离式的方法进行检测和分类，取得了明显的相对提升。

挖掘两阶段和单阶段 HOI 检测的优势

Mining the Benefits of Two-stage and One-stage HOI Detection

Deep neural networks may perform poorly when training datasets are heavily
class-imbalanced. Recently, two-stage methods decouple representation learning
and classifier learning to improve performance. But there is still the vital
issue of miscalibration. To address it, we design two methods to improve
calibration and performance in such scenarios. Motivated by the fact that
predicted probability distributions of classes are highly related to the
numbers of class instances, we propose label-aware smoothing to deal with
different degrees of over-confidence for classes and improve classifier
learning. For dataset bias between these two stages due to different samplers,
we further propose shifted batch normalization in the decoupling framework. Our
proposed methods set new records on multiple popular long-tailed recognition
benchmark datasets, including CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT,
Places-LT, and iNaturalist 2018. Code will be available at
this https URL

本文针对训练数据集不均衡和 miscalibration 等问题，设计了两种方法，分别为标签感知平滑和批次偏移规范化，并在多个长尾识别基准数据集上创造了新记录。