Computer vision applications such as visual relationship detection and human
object interaction can be formulated as a composite (structured) set detection
problem in which both the parts (subject, object, and predicate) and the sum
(triplet as a whole) are to be detected in a hierarchical fashion. In this
paper, we present a new approach, denoted Part-and-Sum detection Transformer
(PST), to perform end-to-end visual composite set detection. Different from
existing Transformers in which queries are at a single level, we simultaneously
model the joint part and sum hypotheses/interactions with composite queries and
attention modules. We explicitly incorporate sum queries to enable better
modeling of the part-and-sum relations that are absent in the standard
Transformers. Our approach also uses novel tensor-based part queries and
vector-based sum queries, and models their joint interaction. We report
experiments on two vision tasks, visual relationship detection and human object
interaction and demonstrate that PST achieves state of the art results among
single-stage models, while nearly matching the results of custom designed
two-stage models.

本文提出了一种名为 PST 的新方法，将计算机视觉应用程序（如视觉关系检测和人物交互）表示为一个复合（结构化）检测问题，在其中，部分和总和均以分级方式被检测。

使用部分和总 Transformer 和复合查询进行视觉关系检测

Visual Relationship Detection Using Part-and-Sum Transformers with  Composite Queries

Detection of double JPEG compression is important to forensics analysis. A
few methods were proposed based on convolutional neural networks (CNNs). These
methods only accept inputs from pre-processed data, such as histogram features
and/or decompressed images. In this paper, we present a CNN solution by using
raw DCT (discrete cosine transformation) coefficients from JPEG images as
input. Considering the DCT sub-band nature in JPEG, a multiple-branch CNN
structure has been designed to reveal whether a JPEG format image has been
doubly compressed. Comparing with previous methods, the proposed method
provides end-to-end detection capability. Extensive experiments have been
carried out to demonstrate the effectiveness of the proposed network.

本文提出了一种使用 JPEG 图像的 DCT 系数作为输入的 CNN 解决方案来检测双 JPEG 压缩的方法，并且相对于以往方法具备端到端的检测能力。