Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models

Pretrained vision-language models, such as CLIP, have demonstrated strong generalization capabilities, making them promising tools in the realm of zero-shot visual recognition. Visual relation detection (VRD) is a typical task that identifies relationship (or interaction) types between object pairs within an image. However, naively utilizing CLIP with prevalent class-based prompts for zero-shot VRD has several weaknesses, e.g., it struggles to distinguish between different fine-grained relation types and it neglects essential spatial information of two objects. To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. Specifically, RECODE first decomposes each predicate category into subject, object, and spatial components. Then, it leverages large language models (LLMs) to generate description-based prompts (or visual cues) for each component. Different visual cues enhance the discriminability of similar relation categories from different perspectives, which significantly boosts performance in VRD. To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues. Extensive experiments on four VRD benchmarks have demonstrated the effectiveness and interpretability of RECODE

Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training

With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality supervision from the noisy DS. Here, we go beyond label noise and identify the key bottleneck of DS-MIL to be its low data utilization: as high-quality supervision being refined by MIL, MIL abandons a large amount of training instances, which leads to a low data utilization and hinders model training from having abundant supervision. In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels. Specifically, since VAT is label-free, we employ the instance-level VAT to recycle instances abandoned by MIL. Besides, we deploy AT at the bag-level to unleash the full potential of the high-quality supervision got by MIL. Our proposed method brings consistent improvements (~ 5 absolute AUC score) to the previous state of the art, which verifies the importance of the data utilization issue and the effectiveness of our method.Comment: Accepted by AAAI 202

Circ_0008657 regulates lung DNA damage induced by hexavalent chromium through the miR-203a-3p/ATM axis

Hexavalent chromium [Cr (VI)] is an important environmental pollutant and may cause lung injury when inhaled into the human body. Cr (VI) is genotoxic and can cause DNA damage, although the underlying epigenetic mechanisms remain unclear. To simulate the real-life workplace exposure to Cr (VI), we used a novel exposure dose calculation method. We evaluated the effect of Cr (VI) on DNA damage in human bronchial epithelial cells (16HBE and BEAS-2B) by calculating the equivalent real-time exposure dose of Cr (VI) (0 to 10 μM) in an environmental population. Comet experiments and olive tail moment measurements revealed increased DNA damage in cells exposed to Cr (VI). Cr (VI) treatment increased nuclear γ-H2AX foci and γ-H2AX protein expression, and caused DNA damage in the lung tissues of mice. An effective Cr (VI) dose (6 μM) was determined and used for cell treatment. Cr (VI) exposure upregulated circ_0008657, and knockdown of circ_0008657 decreased Cr (VI)-induced DNA damage, whereas circ_0008657 overexpression had the opposite effect. Mechanistically, we found that circ_0008657 binds to microRNA (miR)-203a-3p and subsequently regulates ATM serine/threonine kinase (ATM), a key protein involved in homologous recombination repair downstream of miR-203a-3p, thereby regulating DNA damage induced by Cr (VI). The present findings suggest that circ_0008657 competitively binds to miR-203a-3p to activate the ATM pathway and regulate the DNA damage response after environmental chemical exposure in vivo and in vitro

Number of unigenes related to intestinal digestive enzymes.

<p>x-axis indicates the corresponding number of unigenes and y-axis indicates the specific unigenes related to intestinal digestive enzymes. Three hundred and ninety-four protease-related unigenes were identified in <i>M</i>. <i>alternatus</i> Hope transcriptome. <b>(I) Number of specific unigenes related to serine proteases in intestinal digestive enzymes</b>. The icon indicates specific unigenes. The number in brackets indicates the corresponding number of unigenes.</p

Unigenes length distribution.

<p>The y-axis number has been converted into logarithmic scale.</p

Sequence statistics of the Illumina sequencing assembly.

<p>Sequence statistics of the Illumina sequencing assembly.</p

Number of unigenes related to pesticide receptors and resistance-related genes.

<p>x-axis indicates the number of unigenes, y-axis indicates the specific unigenes related to pesticide receptors and resistance-related genes. Seven hundred and fifty-nine insecticide receptors and resistance-related unigenes were identified in <i>M</i>. <i>alternatus</i> transcriptome.</p

Number of unigenes related to immune-related molecules.

<p>x-axis indicates the corresponding number of unigenes and y-axis indicates the specific unigenes related to immune-related molecules. 478 unigenes were identified related to immune molecules and receptors in the transcriptome. This group contains 20 widely recognized immune factors. <b>(I) Number of specific unigenes related to IMD pathway in immune-related molecules. (II) Number of specific unigenes related to Toll pathway in immune-related molecules</b>. The icon indicates specific unigenes. The number in brackets indicates the corresponding number of unigenes.</p