Camouflaged objects adaptively fit their color and texture with the
environment, which makes them indistinguishable from the surroundings. Current
methods revealed that high-level semantic features can highlight the
differences between camouflaged objects and the backgrounds. Consequently, they
integrate high-level semantic features with low-level detailed features for
accurate camouflaged object detection (COD). Unlike previous designs for
multi-level feature fusion, we state that enhancing low-level features is more
impending for COD. In this paper, we propose an overlapped window cross-level
attention (OWinCA) to achieve the low-level feature enhancement guided by the
highest-level features. By sliding an aligned window pair on both the highest-
and low-level feature maps, the high-level semantics are explicitly integrated
into the low-level details via cross-level attention. Additionally, it employs
an overlapped window partition strategy to alleviate the incoherence among
windows, which prevents the loss of global information. These adoptions enable
the proposed OWinCA to enhance low-level features by promoting the separability
of camouflaged objects. The associated proposed OWinCANet fuses these enhanced
multi-level features by simple convolution operation to achieve the final COD.
Experiments conducted on three large-scale COD datasets demonstrate that our
OWinCANet significantly surpasses the current state-of-the-art COD methods.

我们提出了一种重叠窗口跨层级注意力（OWinCA）方法，通过在高级语义特征图和低级细节特征图上滑动对齐的窗口对低级特征进行增强，并通过简单卷积操作融合增强后的多层特征来实现准确的伪装对象检测（COD）。实验结果表明，我们的 OWinCANet 明显超过了当前最先进的 COD 方法。

重叠窗口的交叉级别注意力用于伪装物体检测

Cross-level Attention with Overlapped Windows for Camouflaged Object  Detection

Large language models (LLMs) have shown surprisingly good performance in
multilingual neural machine translation (MNMT) even when trained without
parallel data. Yet, despite the fact that the amount of training data is
gigantic, they still struggle with translating rare words, particularly for
low-resource languages. Even worse, it is usually unrealistic to retrieve
relevant demonstrations for in-context learning with low-resource languages on
LLMs, which restricts the practical use of LLMs for translation -- how should
we mitigate this problem? To this end, we present a novel method, CoD, which
augments LLMs with prior knowledge with the chains of multilingual dictionaries
for a subset of input words to elicit translation abilities for LLMs. Extensive
experiments indicate that augmenting ChatGPT with CoD elicits large gains by up
to 13x ChrF++ points for MNMT (3.08 to 42.63 for English to Serbian written in
Cyrillic script) on FLORES-200 full devtest set. We further demonstrate the
importance of chaining the multilingual dictionaries, as well as the
superiority of CoD to few-shot demonstration for low-resource languages.

本篇论文提出一种新方法名为 CoD，通过使用多语言词典来引出 LLMs 的翻译能力，从而提高大规模语言模型在多语言神经机器翻译中的表现，实验表明，CoD 可使得 ChatGPT 在 FLORES-200 全 devtest 集上英塞语察里洛文翻译的 ChrF++ 分数由 3.08 提高至 42.63，优于为低资源语言提供少量演示的方法。

链式词典提示诱发大型语言模型翻译

Chain-of-Dictionary Prompting Elicits Translation in Large Language  Models

We present the first systematic study on concealed object detection (COD),
which aims to identify objects that are "perfectly" embedded in their
background. The high intrinsic similarities between the concealed objects and
their background make COD far more challenging than traditional object
detection/segmentation. To better understand this task, we collect a
large-scale dataset, called COD10K, which consists of 10,000 images covering
concealed objects in diverse real-world scenarios from 78 object categories.
Further, we provide rich annotations including object categories, object
boundaries, challenging attributes, object-level labels, and instance-level
annotations. Our COD10K is the largest COD dataset to date, with the richest
annotations, which enables comprehensive concealed object understanding and can
even be used to help progress several other vision tasks, such as detection,
segmentation, classification, etc. Motivated by how animals hunt in the wild,
we also design a simple but strong baseline for COD, termed the Search
Identification Network (SINet). Without any bells and whistles, SINet
outperforms 12 cutting-edge baselines on all datasets tested, making them
robust, general architectures that could serve as catalysts for future research
in COD. Finally, we provide some interesting findings and highlight several
potential applications and future directions. To spark research in this new
field, our code, dataset, and online demo are available on our project page:
this http URL

本研究提出了针对被隐藏物体的检测任务，提供了名为 COD10K 的大型数据集，涵盖 78 个物体类别的 10,000 个来自不同真实场景的图像，并提供了详细的注释。其中，Search Identification Network（SINet）是一种简单但强大的基线用于 COD，并在测试的所有数据集上击败了 12 个前沿基线。