3D visual grounding aims to identify the target object within a 3D point
cloud scene referred to by a natural language description. While previous works
attempt to exploit the verbo-visual relation with proposed cross-modal
transformers, unstructured natural utterances and scattered objects might lead
to undesirable performances. In this paper, we introduce DOrA, a novel 3D
visual grounding framework with Order-Aware referring. DOrA is designed to
leverage Large Language Models (LLMs) to parse language description, suggesting
a referential order of anchor objects. Such ordered anchor objects allow DOrA
to update visual features and locate the target object during the grounding
process. Experimental results on the NR3D and ScanRefer datasets demonstrate
our superiority in both low-resource and full-data scenarios. In particular,
DOrA surpasses current state-of-the-art frameworks by 9.3% and 7.8% grounding
accuracy under 1% data and 10% data settings, respectively.

DOrA 是一个使用大型语言模型的 3D 视觉指向框架，通过引入有序锚定对象，更新视觉特征并定位目标对象，在低资源和全数据场景下表现出超越当前最先进框架的优越性，分别在 1％数据和 10％数据设置下将基准提高了 9.3％和 7.8％的准确率。

DOrA：具有顺序感的三维视觉连接

DOrA: 3D Visual Grounding with Order-Aware Referring

Internet memes have become a powerful means for individuals to express
emotions, thoughts, and perspectives on social media. While often considered as
a source of humor and entertainment, memes can also disseminate hateful content
targeting individuals or communities. Most existing research focuses on the
negative aspects of memes in high-resource languages, overlooking the
distinctive challenges associated with low-resource languages like Bengali
(also known as Bangla). Furthermore, while previous work on Bengali memes has
focused on detecting hateful memes, there has been no work on detecting their
targeted entities. To bridge this gap and facilitate research in this arena, we
introduce a novel multimodal dataset for Bengali, BHM (Bengali Hateful Memes).
The dataset consists of 7,148 memes with Bengali as well as code-mixed
captions, tailored for two tasks: (i) detecting hateful memes, and (ii)
detecting the social entities they target (i.e., Individual, Organization,
Community, and Society). To solve these tasks, we propose DORA (Dual cO
attention fRAmework), a multimodal deep neural network that systematically
extracts the significant modality features from the memes and jointly evaluates
them with the modality-specific features to understand the context better. Our
experiments show that DORA is generalizable on other low-resource hateful meme
datasets and outperforms several state-of-the-art rivaling baselines.

提出 Bengali Hateful Memes（BHM）数据集以及用于检测恶意表情包和识别相关社会实体的 DORA 模型，该模型在低资源恶意表情包数据集上表现出色。

解读仇恨：辨识仇恨迷因及其目标

Deciphering Hate: Identifying Hateful Memes and Their Targets

Among the widely used parameter-efficient finetuning (PEFT) methods, LoRA and
its variants have gained considerable popularity because of avoiding additional
inference costs. However, there still often exists an accuracy gap between
these methods and full fine-tuning (FT). In this work, we first introduce a
novel weight decomposition analysis to investigate the inherent differences
between FT and LoRA. Aiming to resemble the learning capacity of FT from the
findings, we propose Weight-Decomposed LowRank Adaptation (DoRA). DoRA
decomposes the pre-trained weight into two components, magnitude and direction,
for fine-tuning, specifically employing LoRA for directional updates to
efficiently minimize the number of trainable parameters. By employing DoRA, we
enhance both the learning capacity and training stability of LoRA while
avoiding any additional inference overhead. DoRA consistently outperforms LoRA
on fine-tuning LLaMA, LLaVA, and VL-BART on various downstream tasks, such as
commonsense reasoning, visual instruction tuning, and image/video-text
understanding.

通过权重分解分析和 LoRA 方法的聚焦更新，DoRA 方法在保持低成本细调的基础上增强了学习能力和训练稳定性，对各种常识推理、视觉指导调整和图像 / 视频 - 文本理解等不同下游任务，超越了 LoRA 方法。