Score Distillation Sampling (SDS) is a recent but already widely popular
method that relies on an image diffusion model to control optimization problems
using text prompts. In this paper, we conduct an in-depth analysis of the SDS
loss function, identify an inherent problem with its formulation, and propose a
surprisingly easy but effective fix. Specifically, we decompose the loss into
different factors and isolate the component responsible for noisy gradients. In
the original formulation, high text guidance is used to account for the noise,
leading to unwanted side effects. Instead, we train a shallow network mimicking
the timestep-dependent denoising deficiency of the image diffusion model in
order to effectively factor it out. We demonstrate the versatility and the
effectiveness of our novel loss formulation through several qualitative and
quantitative experiments, including optimization-based image synthesis and
editing, zero-shot image translation network training, and text-to-3D
synthesis.

Score Distillation Sampling 方法使用图像扩散模型解决优化问题，我们分析了其损失函数并提出有效修复方案，通过定量和定性实验验证了新的损失公式的多样性和有效性。

得分蒸馏采样与学习的流形校正

Score Distillation Sampling with Learned Manifold Corrective

This work proposes a weakly-supervised temporal action localization
framework, called D2-Net, which strives to temporally localize actions using
video-level supervision. Our main contribution is the introduction of a novel
loss formulation, which jointly enhances the discriminability of latent
embeddings and robustness of the output temporal class activations with respect
to foreground-background noise caused by weak supervision. The proposed
formulation comprises a discriminative and a denoising loss term for enhancing
temporal action localization. The discriminative term incorporates a
classification loss and utilizes a top-down attention mechanism to enhance the
separability of latent foreground-background embeddings. The denoising loss
term explicitly addresses the foreground-background noise in class activations
by simultaneously maximizing intra-video and inter-video mutual information
using a bottom-up attention mechanism. As a result, activations in the
foreground regions are emphasized whereas those in the background regions are
suppressed, thereby leading to more robust predictions. Comprehensive
experiments are performed on multiple benchmarks, including THUMOS14 and
ActivityNet1.2. Our D2-Net performs favorably in comparison to the existing
methods on all datasets, achieving gains as high as 2.3% in terms of mAP at
IoU=0.5 on THUMOS14. Source code is available at
this https URL

本文提出了一种名为 D2-Net 的弱监督时间动作本地化框架，其尝试使用视频级监督来对动作进行时间定位。其中，我们的创新主要体现在引入了一个新的损失函数，该函数通过联合增强潜在嵌入的可区分性和输出时间类别激活的鲁棒性，从而应对弱监督引起的前景背景噪声。通过综合多个基准测试数据，包括 THUMOS14 和 ActivityNet1.2，我们的 D2-Net 方法在所有数据集上表现良好，其 mAP 在 IoU = 0.5 时可以达到 2.3% 的提高。

D2-Net: 通过判别嵌入和去噪激活进行弱监督行动定位

D2-Net: Weakly-Supervised Action Localization via Discriminative  Embeddings and Denoised Activations

We present a deep learning method for end-to-end monocular 3D object
detection and metric shape retrieval. We propose a novel loss formulation by
lifting 2D detection, orientation, and scale estimation into 3D space. Instead
of optimizing these quantities separately, the 3D instantiation allows to
properly measure the metric misalignment of boxes. We experimentally show that
our 10D lifting of sparse 2D Regions of Interests (RoIs) achieves great results
both for 6D pose and recovery of the textured metric geometry of instances.
This further enables 3D synthetic data augmentation via inpainting recovered
meshes directly onto the 2D scenes. We evaluate on KITTI3D against other strong
monocular methods and demonstrate that our approach doubles the AP on the 3D
pose metrics on the official test set, defining the new state of the art.

本文提出了一种深度学习方法，它可以通过端到端的单目 3D 物体检测和度量形状检索，通过提出新的损失公式将 2D 检测、方向和尺度估计提升到 3D 空间，并在 KITTI3D 数据集上展示了与其他强大单目方法和新的最佳结果相比，对 6D 姿态和纹理度量几何学的实例恢复表现出了优异的结果。