Video Super-Resolution (VSR) aims to restore high-resolution (HR) videos from low-resolution (LR) videos. Existing VSR techniques usually recover HR frames by extracting pertinent textures from nearby frames with known degradation processes. Despite significant progress, grand challenges are remained to effectively extract and transmit high-quality textures from high-degraded low-quality sequences, such as blur, additive noises, and compression artifacts. In this work, a novel Frequency-Transformer (FTVSR) is proposed for handling low-quality videos that carry out self-attention in a combined space-time-frequency domain. First, video frames are split into patches and each patch is transformed into spectral maps in which each channel represents a frequency band. It permits a fine-grained self-attention on each frequency band, so that real visual texture can be distinguished from artifacts. Second, a novel dual frequency attention (DFA) mechanism is proposed to capture the global frequency relations and local frequency relations, which can handle different complicated degradation processes in real-world scenarios. Third, we explore different self-attention schemes for video processing in the frequency domain and discover that a ``divided attention'' which conducts a joint space-frequency attention before applying temporal-frequency attention, leads to the best video enhancement quality. Extensive experiments on three widely-used VSR datasets show that FTVSR outperforms state-of-the-art methods on different low-quality videos with clear visual margins. Code and pre-trained models are available at https://github.com/researchmm/FTVSR.

Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between classification and localization, make temporally localized results suffer from the serious incomplete issue. To tackle this issue without additional annotations, this paper considers to distill free action knowledge from Vision-Language Pre-training (VLP), since we surprisingly observe that the localization results of vanilla VLP have an over-complete issue, which is just complementary to the CBP results. To fuse such complementarity, we propose a novel distillation-collaboration framework with two branches acting as CBP and VLP respectively. The framework is optimized through a dual-branch alternate training strategy. Specifically, during the B step, we distill the confident background pseudo-labels from the CBP branch; while during the F step, the confident foreground pseudo-labels are distilled from the VLP branch. And as a result, the dual-branch complementarity is effectively fused to promote a strong alliance. Extensive experiments and ablation studies on THUMOS14 and ActivityNet1.2 reveal that our method significantly outperforms state-of-the-art methods.

这项工作的目的是探索如何有效有效地将预训练的基础模型适应图像语义分割的各种下游任务。常规方法通常为每个特定数据集微调整个网络，并且存储这些网络的大量参数是繁重的。最近的一些作品试图将一些可训练的参数插入冷冻网络中，以学习有效调整的视觉提示。但是，这些作品显着修改了标准模块的原始结构，使其在许多现有的高速推理设备上无法使用，其中标准模块及其参数已嵌入。为了促进基于及时的语义细分，我们提出了一个新颖的阶段间及时匹配的框架，该框架保持基础模型的原始结构，同时自适应地生成视觉提示，以适应以任务为导向的调整。具体而言，首先将预训练的模型分为多个阶段，其参数被冷冻并共享所有语义分割任务。然后将称为语义意识的提示匹配器的轻巧模块在两个阶段之间介绍给层次上的插值，以在临时语义图的指导下学习每个特定任务的合理提示。这样，我们可以更好地刺激对冷冻模型的预训练的知识，以有效地学习下游数据集的语义概念。在五个基准上进行的广泛实验表明，所提出的方法可以实现参数效率和性能效率之间的有希望的权衡。

细颗粒的对象检索旨在学习判别性表示以检索视觉上相似的对象。但是，现有的表现最佳作品通常在语义嵌入空间上施加成对的相似性，以在有限数据方面不断调整整个模型，从而使次优溶液易于收敛。在本文中，我们开发了细粒度的检索提示调整（FRPT），该调整引导了一个冷冻的预训练模型，从样本提示和功能适应的角度从样本提示的角度执行精细颗粒的检索任务。具体而言，FRPT只需要在提示中学习更少的参数和适应性，而不是对整个模型进行微调，从而解决了通过微调整个模型引起的次优溶液的收敛性。从技术上讲，随着样本提示，引入结构扰动提示（SPP）以缩放甚至夸大了一些像素，从而通过内容感知到的不均匀采样操作为类别预测做出了贡献。这样，SPP可以通过在原始预训练期间接近已解决的任务的扰动提示来帮助您的精细颗粒检索任务。此外，提出了特定于类别的意识头并将其视为特征适应，它可以使用实例归一化在预训练模型提取的特征中消除物种差异，因此仅使优化的功能仅包括子类别之间的差异。广泛的实验表明，我们的FRPT具有较少的可学习参数，可以在三个广泛使用的细粒数据集上实现最先进的性能。

在计算机视觉中，微调是利用预训练的视觉模型来执行下游任务的事实上的方法。但是，由于采用参数效率低下的全局更新并严重依赖于高质量的下游数据，因此在实践中部署它是非常具有挑战性的。最近，基于及时的学习添加了与任务相关的提示，以使下游任务适应预训练的模型，从而极大地提高了许多自然语言下游任务的性能。在这项工作中，我们扩展了这种显着的转移能力，从迅速的愿景模型中受益，以替代微调。为此，我们提出了参数有效的及时调整（亲调整），以使冷冻视觉模型适应各种下游视觉任务。实行调整的关键是基于及时的调整，即学习特定于任务的视觉提示，以使用预先训练的模型冷冻的下游输入图像。通过仅培训一些其他参数，它可以在基于CNN和基于变压器的各种架构上工作。广泛的实验证据表明，在广泛的视觉任务和场景中，主张表现优于微调，包括图像分类（通用对象，类失衡，图像腐败，对抗性稳定性和分布范围内的概括）和密集的预测任务例如对象检测和语义分割。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced Re-weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 \& V6 show the performances and generality of the SCR with the traditional SGG models.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.