Few-shot object detection (FSOD) aims to extract semantic knowledge from
limited object instances of novel categories within a target domain. Recent
advances in FSOD focus on fine-tuning the base model based on a few objects via
meta-learning or data augmentation. Despite their success, the majority of them
are grounded with parametric readjustment to generalize on novel objects, which
face considerable challenges in Industry 5.0, such as (i) a certain amount of
fine-tuning time is required, and (ii) the parameters of the constructed model
being unavailable due to the privilege protection, making the fine-tuning fail.
Such constraints naturally limit its application in scenarios with real-time
configuration requirements or within black-box settings. To tackle the
challenges mentioned above, we formalize a novel FSOD task, referred to as Test
TIme Few Shot DEtection (TIDE), where the model is un-tuned in the
configuration procedure. To that end, we introduce an asymmetric architecture
for learning a support-instance-guided dynamic category classifier. Further, a
cross-attention module and a multi-scale resizer are provided to enhance the
model performance. Experimental results on multiple few-shot object detection
platforms reveal that the proposed TIDE significantly outperforms existing
contemporary methods. The implementation codes are available at
this https URL

通过引入非对称架构来学习一个支持实例引导的动态类别分类器，进一步提供交叉注意力模块和多尺度调整器以增强模型性能，从而显著优于现有的多个少样本目标检测方法。

TIDE: 测试时间的少样本物体检测

TIDE: Test Time Few Shot Object Detection

The information retrieval community has made significant progress in
improving the efficiency of Dual Encoder (DE) dense passage retrieval systems,
making them suitable for latency-sensitive settings. However, many proposed
procedures are often too complex or resource-intensive, which makes it
difficult for practitioners to adopt them or identify sources of empirical
gains. Therefore, in this work, we propose a trivially simple recipe to serve
as a baseline method for boosting the efficiency of DE retrievers leveraging an
asymmetric architecture. Our results demonstrate that even a 2-layer,
BERT-based query encoder can still retain 92.5% of the full DE performance on
the BEIR benchmark via unsupervised distillation and proper student
initialization. We hope that our findings will encourage the community to
re-evaluate the trade-offs between method complexity and performance
improvements.

本文中，我们提出了一个简单的基线方法，使用不对称架构来提高 Dual Encoder 检索器的效率。我们发现即使使用只有两层的 BERT-based 查询编码器，通过无监督的蒸馏和适当的学生初始化，仍然可以在 BEIR 基准测试中保持 92.5％的 DE 性能。我们希望我们的研究可以鼓励社区重新评估方法复杂性和性能提升之间的权衡。

通过嵌入对齐的查询编码器蒸馏是提升密集检索器在线效率的强大基准方法

Query Encoder Distillation via Embedding Alignment is a Strong Baseline  Method to Boost Dense Retriever Online Efficiency

In real-world systems, scaling has been critical for improving the
translation quality in autoregressive translation (AT), which however has not
been well studied for non-autoregressive translation (NAT). In this work, we
bridge the gap by systematically studying the impact of scaling on NAT
behaviors. Extensive experiments on six WMT benchmarks over two advanced NAT
models show that scaling can alleviate the commonly-cited weaknesses of NAT
models, resulting in better translation performance. To reduce the side-effect
of scaling on decoding speed, we empirically investigate the impact of NAT
encoder and decoder on the translation performance. Experimental results on the
large-scale WMT20 En-De show that the asymmetric architecture (e.g. bigger
encoder and smaller decoder) can achieve comparable performance with the
scaling model, while maintaining the superiority of decoding speed with
standard NAT models. To this end, we establish a new benchmark by validating
scaled NAT models on the scaled dataset, which can be regarded as a strong
baseline for future works. We release code, models and system outputs at
this https URL

本文系统研究了缩放对非自回归翻译的影响，发现缩放可以缓解其模型的弱点，并提升其翻译性能。同时，作者还研究了 NAT 编码器和解码器对翻译性能的影响，并建立了新的基准数据集作为未来研究的强有力基础线。