Semantic image understanding is a challenging topic in computer vision. It
requires to detect all objects in an image, but also to identify all the
relations between them. Detected objects, their labels and the discovered
relations can be used to construct a scene graph which provides an abstract
semantic interpretation of an image. In previous works, relations were
identified by solving an assignment problem formulated as Mixed-Integer Linear
Programs. In this work, we interpret that formulation as Ordinary Differential
Equation (ODE). The proposed architecture performs scene graph inference by
solving a neural variant of an ODE by end-to-end learning. It achieves
state-of-the-art results on all three benchmark tasks: scene graph generation
(SGGen), classification (SGCls) and visual relationship detection (PredCls) on
Visual Genome benchmark.

本文提出了一种使用神经 ODE 求解的架构来进行语义图像理解，达到了当前三个基准任务的最佳结果，并构建了一个提供图像抽象语义解释的场景图。

NODIS: 神经常微分场景理解

NODIS: Neural Ordinary Differential Scene Understanding

It has long been considered a significant problem to improve the visual
quality of lossy image and video compression. Recent advances in computing
power together with the availability of large training data sets has increased
interest in the application of deep learning cnns to address image recognition
and image processing tasks. Here, we present a powerful cnn tailored to the
specific task of semantic image understanding to achieve higher visual quality
in lossy compression. A modest increase in complexity is incorporated to the
encoder which allows a standard, off-the-shelf jpeg decoder to be used. While
jpeg encoding may be optimized for generic images, the process is ultimately
unaware of the specific content of the image to be compressed. Our technique
makes jpeg content-aware by designing and training a model to identify multiple
semantic regions in a given image. Unlike object detection techniques, our
model does not require labeling of object positions and is able to identify
objects in a single pass. We present a new cnn architecture directed
specifically to image compression, which generates a map that highlights
semantically-salient regions so that they can be encoded at higher quality as
compared to background regions. By adding a complete set of features for every
class, and then taking a threshold over the sum of all feature activations, we
generate a map that highlights semantically-salient regions so that they can be
encoded at a better quality compared to background regions. Experiments are
presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset,
in which our algorithm achieves higher visual quality for the same compressed
size.

应用深度学习的方法解决在损失图像和视频压缩中提高视觉质量的问题，通过训练一个特定的卷积神经网络，实现对图像语义的理解，并通过对每个对象训练特征的方式生成高质量的压缩图像。

使用深度卷积网络的语义感知图像压缩

Semantic Perceptual Image Compression using Deep Convolution Networks

While natural beauty is often considered a subjective property of images, in
this paper, we take an objective approach and provide methods for quantifying
and predicting the scenicness of an image. Using a dataset containing hundreds
of thousands of outdoor images captured throughout Great Britain with
crowdsourced ratings of natural beauty, we propose an approach to predict
scenicness which explicitly accounts for the variance of human ratings. We
demonstrate that quantitative measures of scenicness can benefit semantic image
understanding, content-aware image processing, and a novel application of
cross-view mapping, where the sparsity of ground-level images can be addressed
by incorporating unlabeled overhead images in the training and prediction
steps. For each application, our methods for scenicness prediction result in
quantitative and qualitative improvements over baseline approaches.

本论文提供了一种量化和预测图像美学的方法，该方法可以有效地应用于图像处理领域，特别是语义图像理解、内容感知图像处理和跨视图映射等领域，并将这些应用与基线方法进行对比。

自然美学理解与映射

Understanding and Mapping Natural Beauty

Current approaches to semantic image and scene understanding typically employ
rather simple object representations such as 2D or 3D bounding boxes. While
such coarse models are robust and allow for reliable object detection, they
discard much of the information about objects' 3D shape and pose, and thus do
not lend themselves well to higher-level reasoning. Here, we propose to base
scene understanding on a high-resolution object representation. An object class
- in our case cars - is modeled as a deformable 3D wireframe, which enables
fine-grained modeling at the level of individual vertices and faces. We augment
that model to explicitly include vertex-level occlusion, and embed all
instances in a common coordinate frame, in order to infer and exploit
object-object interactions. Specifically, from a single view we jointly
estimate the shapes and poses of multiple objects in a common 3D frame. A
ground plane in that frame is estimated by consensus among different objects,
which significantly stabilizes monocular 3D pose estimation. The fine-grained
model, in conjunction with the explicit 3D scene model, further allows one to
infer part-level occlusions between the modeled objects, as well as occlusions
by other, unmodeled scene elements. To demonstrate the benefits of such
detailed object class models in the context of scene understanding we
systematically evaluate our approach on the challenging KITTI street scene
dataset. The experiments show that the model's ability to utilize image
evidence at the level of individual parts improves monocular 3D pose estimation
w.r.t. both location and (continuous) viewpoint.

本文提出了一种基于 3D 可变形线框的高分辨率车辆对象表示，该表示能精细地建模单个点和面的水平，结合此表示和明确的 3D 场景模型，我们能够对场景进行更细致和准确的理解并从单个视角评估多个对象的位置与视点的单眼 3D 姿态估计。