A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusion model which learns to refine the output spectrogram of an existing TTS model (e.g., FastSpeech 2) by predicting the residual between the model output and the corresponding ground-truth speech. ResGrad has several advantages: 1) Compare with other acceleration methods for DDPM which need to synthesize speech from scratch, ResGrad reduces the complexity of task by changing the generation target from ground-truth mel-spectrogram to the residual, resulting into a more lightweight model and thus a smaller real-time factor. 2) ResGrad is employed in the inference process of the existing TTS model in a plug-and-play way, without re-training this model. We verify ResGrad on the single-speaker dataset LJSpeech and two more challenging datasets with multiple speakers (LibriTTS) and high sampling rate (VCTK). Experimental results show that in comparison with other speed-up methods of DDPMs: 1) ResGrad achieves better sample quality with the same inference speed measured by real-time factor; 2) with similar speech quality, ResGrad synthesizes speech faster than baseline methods by more than 10 times. Audio samples are available at https://resgrad1.github.io/.

Pre-trained language models achieve superior performance, but they are computationally expensive due to their large size. Techniques such as pruning and knowledge distillation (KD) have been developed to reduce their size and latency. In most structural pruning methods, the pruning units, such as attention heads and feed-forward hidden dimensions, only span a small model structure space and limit the structures that the pruning algorithm can explore. In this work, we propose Gradient-based Intra-attention pruning (GRAIN), which inspects fine intra-attention structures, and allows different heads to have different sizes. Intra-attention pruning greatly expands the searching space of model structures and yields highly heterogeneous structures. We further propose structure regularization to encourage generating more regular structures, which achieves higher speedups than heterogeneous ones. We also integrate KD into the pruning process with a gradient separation strategy to reduce the interference of KD with the pruning process. GRAIN is evaluated on a variety of tasks. Results show that it notably outperforms other methods at the same or similar model size. Even under extreme compression where only $3\%$ weights in transformers remain, the pruned model is still competitive.

Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

The long-standing theory that a colour-naming system evolves under the dual pressure of efficient communication and perceptual mechanism is supported by more and more linguistic studies including the analysis of four decades' diachronic data from the Nafaanra language. This inspires us to explore whether artificial intelligence could evolve and discover a similar colour-naming system via optimising the communication efficiency represented by high-level recognition performance. Here, we propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining the accuracy of machine recognition on the quantised images. Given an RGB image, Annotation Branch maps it into an index map before generating the quantised image with a colour palette, meanwhile the Palette Branch utilises a key-point detection way to find proper colours in palette among whole colour space. By interacting with colour annotation, CQFormer is able to balance both the machine vision accuracy and colour perceptual structure such as distinct and stable colour distribution for discovered colour system. Very interestingly, we even observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Besides, our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage while maintaining a high performance in high-level recognition tasks such as classification and detection. Extensive experiments demonstrate the superior performance of our method with extremely low bit-rate colours. We will release the source code soon.

A key barrier to using reinforcement learning (RL) in many real-world applications is the requirement of a large number of system interactions to learn a good control policy. Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data. However, their performances suffer from the lack of exploration and the distributional shifts in trajectories once controllers are updated. Moreover, most RL methods require that all states are directly observed, which is difficult to be attained in many settings. To overcome these challenges, we propose a trajectory generation algorithm, which adaptively generates new trajectories as if the system is being operated and explored under the updated control policies. Motivated by the fundamental lemma for linear systems, assuming sufficient excitation, we generate trajectories from linear combinations of historical trajectories. For linear feedback control, we prove that the algorithm generates trajectories with the exact distribution as if they are sampled from the real system using the updated control policy. In particular, the algorithm extends to systems where the states are not directly observed. Experiments show that the proposed method significantly reduces the number of sampled data needed for RL algorithms.

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. In other words, the multilingual multimodal machine translation (Multilingual MMT) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for multiple languages. Besides, the image modality has no language boundaries, which is superior to bridging the semantic gap between languages. To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages. Then, an effective baseline LVP-M3 using visual prompts is proposed to support translations between different languages, which includes three stages (token encoding, language-aware visual prompt generation, and language translation). Extensive experimental results on our constructed benchmark datasets demonstrate the effectiveness of LVP-M3 method for Multilingual MMT.

异常识别高度取决于对象与场景之间的关系，因为相同/不同场景中的不同/相同对象动作可能导致各种程度的正态性和异常。因此，对象场景关系实际上在异常检测中起着至关重要的作用，但在以前的工作中探讨了不足。在本文中，我们提出了一个时空关系学习（STRL）框架来解决视频异常检测任务。首先，考虑到对象的动态特征以及场景区域，我们构建了一个时空自动编码器（STAE），以共同利用代表学习的空间和时间演化模式。为了获得更好的图案提取，在STAE模块中设计了两个解码分支，即通过直接预测下一个帧来捕获空间提示的外观分支，以及一个运动分支，重点是通过光流预测对动态进行建模。然后，为了很好地融合对象场所关系，设计了一个关系学习（RL）模块来通过引入知识图嵌入方法来分析和总结正常关系。在此过程中具体来说，通过共同建模对象/场景特征和优化的对象场所关系图来衡量对象场景关系的合理性。在三个公共数据集上进行了广泛的实验，而对最新方法的优越性能证明了我们方法的有效性。

尖峰神经网络（SNNS）模仿大脑计算策略，并在时空信息处理中表现出很大的功能。作为人类感知的基本因素，视觉关注是指生物视觉系统中显着区域的动态选择过程。尽管视觉注意力的机制在计算机视觉上取得了巨大成功，但很少会引入SNN中。受到预测注意重新映射的实验观察的启发，我们在这里提出了一种新的时空通道拟合注意力（SCTFA）模块，该模块可以通过使用历史积累的空间通道信息来指导SNN有效地捕获潜在的目标区域。通过在三个事件流数据集（DVS手势，SL-Animals-DVS和MNIST-DVS）上进行系统评估，我们证明了带有SCTFA模块（SCTFA-SNN）的SNN不仅显着超过了基线SNN（BL-SNN）（BL-SNN）（BL-SNN）以及其他两个具有退化注意力模块的SNN模型，但也通过现有最新方法实现了竞争精度。此外，我们的详细分析表明，所提出的SCTFA-SNN模型对噪声和出色的稳定性具有强大的稳健性，同时保持了可接受的复杂性和效率。总体而言，这些发现表明，适当纳入大脑的认知机制可能会提供一种有希望的方法来提高SNN的能力。

近年来，由渠道状态信息（CSI）启用了基于WiFi的智能人类传感技术（CSI）。但是，在不同的环境中部署时，基于CSI的传感系统会遭受性能降解。现有作品通过使用新环境中的大量未标记的高质量数据来通过域的适应来解决这一问题，这在实践中通常不可用。在本文中，我们提出了一种新颖的增强环境不变的鲁棒wifi wifi识别系统，名为Airfi，该系统从新的角度涉及环境依赖问题。 Airfi是一个新颖的领域泛化框架，无论环境如何，都可以学习CSI的关键部分，并将模型推广到看不见的场景，不需要收集任何数据以适应新环境。 Airfi从几个培训环境环境中提取了共同的功能，并最大程度地减少了它们之间的分布差异。该功能将进一步增强，以使环境更强大。此外，可以通过几次学习技术进一步改进该系统。与最先进的方法相比，Airfi能够在不同的环境环境中工作，而无需从新环境中获取任何CSI数据。实验结果表明，我们的系统在新环境中保持强大，并优于比较系统。