Document retrieval in many languages has been largely relying on
multi-lingual models, and leveraging the vast wealth of English training data.
In Japanese, the best performing deep-learning based retrieval approaches rely
on multilingual dense embeddings. In this work, we introduce (1) a
hard-negative augmented version of the Japanese MMARCO dataset and (2)
JaColBERT, a document retrieval model built on the ColBERT model architecture,
specifically for Japanese. JaColBERT vastly outperform all previous monolingual
retrieval approaches and competes with the best multilingual methods, despite
unfavourable evaluation settings (out-of-domain vs. in-domain for the
multilingual models). JaColBERT reaches an average Recall@10 of 0.813,
noticeably ahead of the previous monolingual best-performing model (0.716) and
only slightly behind multilingual-e5-base (0.820), though more noticeably
behind multilingual-e5-large (0.856). These results are achieved using only a
limited, entirely Japanese, training set, more than two orders of magnitudes
smaller than multilingual embedding models. We believe these results show great
promise to support retrieval-enhanced application pipelines in a wide variety
of domains.

JaColBERT, a document retrieval model specifically designed for Japanese, outperforms previous monolingual retrieval approaches and competes with multilingual methods, demonstrating great promise in supporting retrieval-enhanced application pipelines.

JaColBERT 和 Hard Negatives：更好的检索性日语优先嵌入

JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings  for Retrieval: Early Technical Report

The task of open-vocabulary object-centric image retrieval involves the
retrieval of images containing a specified object of interest, delineated by an
open-set text query. As working on large image datasets becomes standard,
solving this task efficiently has gained significant practical importance.
Applications include targeted performance analysis of retrieved images using
ad-hoc queries and hard example mining during training. Recent advancements in
contrastive-based open vocabulary systems have yielded remarkable
breakthroughs, facilitating large-scale open vocabulary image retrieval.
However, these approaches use a single global embedding per image, thereby
constraining the system's ability to retrieve images containing relatively
small object instances. Alternatively, incorporating local embeddings from
detection pipelines faces scalability challenges, making it unsuitable for
retrieval from large databases.
In this work, we present a simple yet effective approach to object-centric
open-vocabulary image retrieval. Our approach aggregates dense embeddings
extracted from CLIP into a compact representation, essentially combining the
scalability of image retrieval pipelines with the object identification
capabilities of dense detection methods. We show the effectiveness of our
scheme to the task by achieving significantly better results than global
feature approaches on three datasets, increasing accuracy by up to 15 mAP
points. We further integrate our scheme into a large scale retrieval framework
and demonstrate our method's advantages in terms of scalability and
interpretability.

本研究提出了一种简单而有效的方法来实现以对象为中心的开放词汇图像检索，通过从 CLIP 中提取出的密集嵌入将大规模图像检索管道的可扩展性与密集检测方法的对象识别能力相结合，从而实现了极大的提升。

聚合特征的面向对象的开放词汇图像检索

Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features

Recently, self-supervised methods show remarkable achievements in image-level
representation learning. Nevertheless, their image-level self-supervisions lead
the learned representation to sub-optimal for dense prediction tasks, such as
object detection, instance segmentation, etc. To tackle this issue, several
recent self-supervised learning methods have extended image-level single
embedding to pixel-level dense embeddings. Unlike image-level representation
learning, due to the spatial deformation of augmentation, it is difficult to
sample pixel-level positive pairs. Previous studies have sampled pixel-level
positive pairs using the winner-takes-all among similarity or thresholding
warped distance between dense embeddings. However, these naive methods can be
struggled by background clutter and outliers problems. In this paper, we
introduce Hough Contrastive Learning (HoughCL), a Hough space based method that
enforces geometric consistency between two dense features. HoughCL achieves
robustness against background clutter and outliers. Furthermore, compared to
baseline, our dense positive pairing method has no additional learnable
parameters and has a small extra computation cost. Compared to previous works,
our method shows better or comparable performance on dense prediction
fine-tuning tasks.

本文介绍了一种基于 Hough 空间的对比学习方法，可以实现对于密集预测任务的图像级自我监督学习的表征有效性，并且相对于之前的方法，能够更好地应对背景混杂和异常值问题。

HoughCL：在密集自监督学习中找到更好的正样本对

HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning

Cross-lingual language tasks typically require a substantial amount of
annotated data or parallel translation data. We explore whether language
representations that capture relationships among languages can be learned and
subsequently leveraged in cross-lingual tasks without the use of parallel data.
We generate dense embeddings for 29 languages using a denoising autoencoder,
and evaluate the embeddings using the World Atlas of Language Structures (WALS)
and two extrinsic tasks in a zero-shot setting: cross-lingual dependency
parsing and cross-lingual natural language inference.

本研究探究是否可以通过学习表示语言之间关系的语言表示来进行跨语言任务，而无需使用平行数据。使用去噪自编码器产生 29 种语言的密集嵌入，并在零样本情况下使用 WALS 和两个外部任务（跨语言依赖解析和跨语言自然语言推理）来评估嵌入。