Few-shot object detection~(FSOD), which aims to detect novel objects with
limited annotated instances, has made significant progress in recent years.
However, existing methods still suffer from biased representations, especially
for novel classes in extremely low-shot scenarios. During fine-tuning, a novel
class may exploit knowledge from similar base classes to construct its own
feature distribution, leading to classification confusion and performance
degradation. To address these challenges, we propose a fine-tuning based FSOD
framework that utilizes semantic embeddings for better detection. In our
proposed method, we align the visual features with class name embeddings and
replace the linear classifier with our semantic similarity classifier. Our
method trains each region proposal to converge to the corresponding class
embedding. Furthermore, we introduce a multimodal feature fusion to augment the
vision-language communication, enabling a novel class to draw support
explicitly from well-trained similar base classes. To prevent class confusion,
we propose a semantic-aware max-margin loss, which adaptively applies a margin
beyond similar classes. As a result, our method allows each novel class to
construct a compact feature space without being confused with similar base
classes. Extensive experiments on Pascal VOC and MS COCO demonstrate the
superiority of our method.

提出了一个使用语义嵌入进行精细调整的少样本目标检测框架，通过使用语义相似性分类器，多模态特征融合和语义感知最大边界损失，解决了现有方法在极低样本场景中对新类别存在的分类混淆和性能下降问题。

语义增强的少样本目标检测

Semantic Enhanced Few-shot Object Detection

Large Language Models (LLMs) have been observed to encode and perpetuate
harmful associations present in the training data. We propose a theoretically
grounded framework called StereoMap to gain insights into their perceptions of
how demographic groups have been viewed by society. The framework is grounded
in the Stereotype Content Model (SCM); a well-established theory from
psychology. According to SCM, stereotypes are not all alike. Instead, the
dimensions of Warmth and Competence serve as the factors that delineate the
nature of stereotypes. Based on the SCM theory, StereoMap maps LLMs'
perceptions of social groups (defined by socio-demographic features) using the
dimensions of Warmth and Competence. Furthermore, the framework enables the
investigation of keywords and verbalizations of reasoning of LLMs' judgments to
uncover underlying factors influencing their perceptions. Our results show that
LLMs exhibit a diverse range of perceptions towards these groups, characterized
by mixed evaluations along the dimensions of Warmth and Competence.
Furthermore, analyzing the reasonings of LLMs, our findings indicate that LLMs
demonstrate an awareness of social disparities, often stating statistical data
and research findings to support their reasoning. This study contributes to the
understanding of how LLMs perceive and represent social groups, shedding light
on their potential biases and the perpetuation of harmful associations.

大语言模型（LLMs）如何感知和表达社会群体，及其潜在偏见和有害联想的维度研究。

立体地图：量化大型语言模型中类人种族刻板印象的认知

StereoMap: Quantifying the Awareness of Human-like Stereotypes in Large  Language Models

Trained classification models can unintentionally lead to biased
representations and predictions, which can reinforce societal preconceptions
and stereotypes. Existing debiasing methods for classification models, such as
adversarial training, are often expensive to train and difficult to optimise.
In this paper, we propose a method for mitigating bias in classifier training
by incorporating contrastive learning, in which instances sharing the same
class label are encouraged to have similar representations, while instances
sharing a protected attribute are forced further apart. In such a way our
method learns representations which capture the task label in focused regions,
while ensuring the protected attribute has diverse spread, and thus has limited
impact on prediction and thereby results in fairer models. Extensive
experimental results across four tasks in NLP and computer vision show (a) that
our proposed method can achieve fairer representations and realises bias
reductions compared with competitive baselines; and (b) that it can do so
without sacrificing main task performance; (c) that it sets a new
state-of-the-art performance in one task despite reducing the bias. Finally,
our method is conceptually simple and agnostic to network architectures, and
incurs minimal additional compute cost.

本论文提出了一种采用对比学习的分类器去偏置化的方法，使得模型在保持主要任务性能的前提下，更加公平地代表输入的各种属性，并取得了在 NLP 和计算机视觉四个任务中的优异表现。