Recently, Class-Agnostic Counting (CAC) problem has garnered increasing
attention owing to its intriguing generality and superior efficiency compared
to Category-Specific Counting (CSC). This paper proposes a novel ExpressCount
to enhance zero-shot object counting by delving deeply into language-guided
exemplar learning. Specifically, the ExpressCount is comprised of an innovative
Language-oriented Exemplar Perceptron and a downstream visual Zero-shot
Counting pipeline. Thereinto, the perceptron hammers at exploiting accurate
exemplar cues from collaborative language-vision signals by inheriting rich
semantic priors from the prevailing pre-trained Large Language Models (LLMs),
whereas the counting pipeline excels in mining fine-grained features through
dual-branch and cross-attention schemes, contributing to the high-quality
similarity learning. Apart from building a bridge between the LLM in vogue and
the visual counting tasks, expression-guided exemplar estimation significantly
advances zero-shot learning capabilities for counting instances with arbitrary
classes. Moreover, devising a FSC-147-Express with annotations of meticulous
linguistic expressions pioneers a new venue for developing and validating
language-based counting models. Extensive experiments demonstrate the
state-of-the-art performance of our ExpressCount, even showcasing the accuracy
on par with partial CSC models.

通过深入研究语言引导示例学习，论文提出了一种新颖的 ExpressCount，用于改进零样本物体计数。通过语言导向的示例感知器和下游视觉零样本计数管道构成的 ExpressCount，在寻找细粒度特征方面表现出色，充分利用了来自大型语言模型的丰富语义先验知识，并显著提升了任意类别的零样本学习能力，从而为发展和验证基于语言的计数模型开辟了新的途径。大量实验证明了 ExpressCount 的最先进性能，甚至展示了与部分特定类别计数模型相媲美的准确性。

通过语言引导的模板学习提升零样本计数

Enhancing Zero-shot Counting via Language-guided Exemplar Learning

Exemplar learning of visual similarities in an unsupervised manner is a
problem of paramount importance to Computer Vision. In this context, however,
the recent breakthrough in deep learning could not yet unfold its full
potential. With only a single positive sample, a great imbalance between one
positive and many negatives, and unreliable relationships between most samples,
training of Convolutional Neural networks is impaired. In this paper we use
weak estimates of local similarities and propose a single optimization problem
to extract batches of samples with mutually consistent relations. Conflicting
relations are distributed over different batches and similar samples are
grouped into compact groups. Learning visual similarities is then framed as a
sequence of categorization tasks. The CNN then consolidates transitivity
relations within and between groups and learns a single representation for all
samples without the need for labels. The proposed unsupervised approach has
shown competitive performance on detailed posture analysis and object
classification.

本文提出了一种利用局部相似性的弱估计，通过单个最优化问题提取具有一致性关系的样本批次，将视觉相似性学习作为分类任务的序列，并使 CNN 巩固群体内和群组间的传递性关系，学习所有样本的单个表示。

深度无监督学习视觉相似性

Deep Unsupervised Learning of Visual Similarities

Exemplar learning is a powerful paradigm for discovering visual similarities
in an unsupervised manner. In this context, however, the recent breakthrough in
deep learning could not yet unfold its full potential. With only a single
positive sample, a great imbalance between one positive and many negatives, and
unreliable relationships between most samples, training of Convolutional Neural
networks is impaired. Given weak estimates of local distance we propose a
single optimization problem to extract batches of samples with mutually
consistent relations. Conflicting relations are distributed over different
batches and similar samples are grouped into compact cliques. Learning exemplar
similarities is framed as a sequence of clique categorization tasks. The CNN
then consolidates transitivity relations within and between cliques and learns
a single representation for all samples without the need for labels. The
proposed unsupervised approach has shown competitive performance on detailed
posture analysis and object classification.

本文提出了一种基于团伙分类任务的无监督训练方法，用于解决在仅有单个正样本、多个负样本以及不可靠的标注关系之间训练卷积神经网络的不足，并将其应用于姿态分析和物体分类任务。

CliqueCNN: 深度无监督样本学习

CliqueCNN: Deep Unsupervised Exemplar Learning

Intuitive observations show that a baby may inherently possess the capability
of recognizing a new visual concept (e.g., chair, dog) by learning from only
very few positive instances taught by parent(s) or others, and this recognition
capability can be gradually further improved by exploring and/or interacting
with the real instances in the physical world. Inspired by these observations,
we propose a computational model for slightly-supervised object detection,
based on prior knowledge modelling, exemplar learning and learning with video
contexts. The prior knowledge is modeled with a pre-trained Convolutional
Neural Network (CNN). When very few instances of a new concept are given, an
initial concept detector is built by exemplar learning over the deep features
from the pre-trained CNN. Simulating the baby's interaction with physical
world, the well-designed tracking solution is then used to discover more
diverse instances from the massive online unlabeled videos. Once a positive
instance is detected/identified with high score in each video, more variable
instances possibly from different view-angles and/or different distances are
tracked and accumulated. Then the concept detector can be fine-tuned based on
these new instances. This process can be repeated again and again till we
obtain a very mature concept detector. Extensive experiments on Pascal
VOC-07/10/12 object detection datasets well demonstrate the effectiveness of
our framework. It can beat the state-of-the-art full-training based
performances by learning from very few samples for each object category, along
with about 20,000 unlabeled videos.

本文提出了一种基于先前知识建模、示例学习和视频上下文学习的轻度半监督目标检测的计算模型，通过与现实实例的交互，从未标记的视频中检测并识别出更多的实例，进一步提高识别能力。