We propose an interactive editing method that allows humans to help deep neural networks (DNNs) learn a latent space more consistent with human knowledge, thereby improving classification accuracy on indistinguishable ambiguous data. Firstly, we visualize high-dimensional data features through dimensionality reduction methods and design an interactive system \textit{SpaceEditing} to display the visualized data. \textit{SpaceEditing} provides a 2D workspace based on the idea of spatial layout. In this workspace, the user can move the projection data in it according to the system guidance. Then, \textit{SpaceEditing} will find the corresponding high-dimensional features according to the projection data moved by the user, and feed the high-dimensional features back to the network for retraining, therefore achieving the purpose of interactively modifying the high-dimensional latent space for the user. Secondly, to more rationally incorporate human knowledge into the training process of neural networks, we design a new loss function that enables the network to learn user-modified information. Finally, We demonstrate how \textit{SpaceEditing} meets user needs through three case studies while evaluating our proposed new method, and the results confirm the effectiveness of our method.

Scene understanding is an essential and challenging task in computer vision. To provide the visually fundamental graphical structure of an image, the scene graph has received increased attention due to its powerful semantic representation. However, it is difficult to draw a proper scene graph for image retrieval, image generation, and multi-modal applications. The conventional scene graph annotation interface is not easy to use in image annotations, and the automatic scene graph generation approaches using deep neural networks are prone to generate redundant content while disregarding details. In this work, we propose SGDraw, a scene graph drawing interface using object-oriented scene graph representation to help users draw and edit scene graphs interactively. For the proposed object-oriented representation, we consider the objects, attributes, and relationships of objects as a structural unit. SGDraw provides a web-based scene graph annotation and generation tool for scene understanding applications. To verify the effectiveness of the proposed interface, we conducted a comparison study with the conventional tool and the user experience study. The results show that SGDraw can help generate scene graphs with richer details and describe the images more accurately than traditional bounding box annotations. We believe the proposed SGDraw can be useful in various vision tasks, such as image retrieval and generation.

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. These methods achieve surprisingly good performance and are shown to be more stable than their corresponding fully fine-tuned counterparts. However, such kind of methods is still not well understood. Some natural questions arise: How does the parameter sparsity lead to promising performance? Why is the model more stable than the fully fine-tuned models? How to choose the tunable parameters? In this paper, we first categorize the existing methods into random approaches, rule-based approaches, and projection-based approaches based on how they choose which parameters to tune. Then, we show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them. We indicate that the sparsity is actually imposing a regularization on the original model by controlling the upper bound of the stability. Such stability leads to better generalization capability which has been empirically observed in a lot of recent research works. Despite the effectiveness of sparsity grounded by our theory, it still remains an open problem of how to choose the tunable parameters. To better choose the tunable parameters, we propose a novel Second-order Approximation Method (SAM) which approximates the original problem with an analytically solvable optimization function. The tunable parameters are determined by directly optimizing the approximation function. The experimental results show that our proposed SAM model outperforms many strong baseline models and it also verifies our theoretical analysis.

Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications. To tackle this limitation, pioneering works have developed handcrafted multiplication-free DNNs, which require expert knowledge and time-consuming manual iteration, calling for fast development tools. To this end, we propose a Neural Architecture Search and Acceleration framework dubbed NASA, which enables automated multiplication-reduced DNN development and integrates a dedicated multiplication-reduced accelerator for boosting DNNs' achievable efficiency. Specifically, NASA adopts neural architecture search (NAS) spaces that augment the state-of-the-art one with hardware-inspired multiplication-free operators, such as shift and adder, armed with a novel progressive pretrain strategy (PGP) together with customized training recipes to automatically search for optimal multiplication-reduced DNNs; On top of that, NASA further develops a dedicated accelerator, which advocates a chunk-based template and auto-mapper dedicated for NASA-NAS resulting DNNs to better leverage their algorithmic properties for boosting hardware efficiency. Experimental results and ablation studies consistently validate the advantages of NASA's algorithm-hardware co-design framework in terms of achievable accuracy and efficiency tradeoffs. Codes are available at https://github.com/GATECH-EIC/NASA.

Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting their achievable hardware efficiency. Meanwhile, existing accelerators dedicated to NLP Transformers are not optimal for ViTs. This is because there is a large difference between ViTs and NLP Transformers: ViTs have a relatively fixed number of input tokens, whose attention maps can be pruned by up to 90% even with fixed sparse patterns; while NLP Transformers need to handle input sequences of varying numbers of tokens and rely on on-the-fly predictions of dynamic sparse attention patterns for each input to achieve a decent sparsity (e.g., >=50%). To this end, we propose a dedicated algorithm and accelerator co-design framework dubbed ViTCoD for accelerating ViTs. Specifically, on the algorithm level, ViTCoD prunes and polarizes the attention maps to have either denser or sparser fixed patterns for regularizing two levels of workloads without hurting the accuracy, largely reducing the attention computations while leaving room for alleviating the remaining dominant data movements; on top of that, we further integrate a lightweight and learnable auto-encoder module to enable trading the dominant high-cost data movements for lower-cost computations. On the hardware level, we develop a dedicated accelerator to simultaneously coordinate the enforced denser/sparser workloads and encoder/decoder engines for boosted hardware utilization. Extensive experiments and ablation studies validate that ViTCoD largely reduces the dominant data movement costs, achieving speedups of up to 235.3x, 142.9x, 86.0x, 10.1x, and 6.8x over general computing platforms CPUs, EdgeGPUs, GPUs, and prior-art Transformer accelerators SpAtten and Sanger under an attention sparsity of 90%, respectively.

在交互环境中学习操纵3D对象一直是强化学习（RL）的挑战性问题。特别是，很难训练可以概括具有不同语义类别，多样形状几何形状和多功能功能的对象的策略。最近，视觉负担能力的技术在提供有效的可操作语义方面提供了以对象为中心的信息先验的前景。因此，可以通过知道如何在手柄上施加力来训练有效的政策来打开门。但是，要学习负担能力，它通常需要人为定义的动作基础，这限制了适用的任务范围。在这项研究中，我们通过使用RL训练过程中生成的联系信息来预测感兴趣的接触图，利用视觉负担。然后，这种联系预测过程会导致一个端到端的负担能力学习框架，该框架可以概括不同类型的操纵任务。令人惊讶的是，这种框架的有效性即使在多阶段和多代理场景下也具有。我们对八种类型的操纵任务进行了测试。结果表明，我们的方法优于基线算法，包括基于视觉的负担方法和RL方法，其成功率很大。演示可以在https://sites.google.com/view/rlafford/上找到。

自动化技术（例如人工智能（AI）和机器人技术）的快速进步构成了越来越多的职业自动化风险，可能会对劳动力市场产生重大影响。最近的社会经济研究表明，接下来的十年中，将近50％的职业处于自动化的高风险。但是，缺乏颗粒状数据和经验知情的模型限制了这些研究的准确性，并使预测哪些工作将是自动化的。在本文中，我们通过在自动化和非自动化职业之间执行分类任务来研究职业的自动化风险。可用信息是由标准职业分类（SOC）分类的910个职业的任务声明，技能和互动。要充分利用此信息，我们提出了一个基于图的半监督分类方法，名为\ textbf {a} utomated \ textbf {o} ccupation \ textbf {c}基于\ textbf {g} rassification \ textbf {n} etworks（\ textbf {aoc-gcn}）识别职业的自动化风险。该模型集成了一个异质图，以捕获职业的本地和全球环境。结果表明，我们提出的方法通过考虑职业的内部特征及其外部互动的信息来优于基线模型。这项研究可以帮助决策者在进入就业市场之前确定潜在的自动化职业并支持个人的决策。

图像文本检索（ITR）在桥接视觉和舌形式方面具有挑战性。对比度学习已被大多数先前的艺术所采用。除了有限的负面图像文本对外，约束学习的能力受到手动加权负对以及对外部知识的不认识的限制。在本文中，我们提出了新型耦合多样性敏感的动量约束学习（编码器），以改善跨模式表示。首先，发明了一种新颖的多样性对比度学习（DCL）体系结构。我们引入了两种模式的动态词典，以扩大图像文本对的比例，并且通过自适应负面对加权实现多样性敏感性。此外，编码器设计了两个分支。一个人从图像/文本中学习实例级的嵌入式，它还基于其嵌入为其输入图像/文本生成伪在线聚类标签。同时，另一个分支学会从常识知识图中查询以形成两种模式的概念级描述符。之后，两个分支都利用DCL来对齐跨模式嵌入空间，而额外的伪聚类标签预测损失则用于促进第二个分支的概念级表示学习。在两个流行的基准测试（即Mscoco和Flicker30k）上进行的广泛实验，验证编码器的表现明显优于最先进的方法。

图形学习模型是研究人员探索图形结构数据的关键工具。为了训练功能强大的图形学习模型，常规方法使用足够的训练数据来训练单个设备上的图形模型。但是，由于隐私问题，在实际情况下这样做是令人难以置信的。联合学习提供了一种可行的解决方案，可以通过引入各种隐私性机制（例如图形边缘的差异隐私）来解决此类限制。然而，联合图学习中的差异隐私可确保图表中维护的分类信息。它降低了图形学习模型的性能。在本文中，我们研究了如何在图形边缘实施差异隐私，并观察实验中的性能下降。我们还注意到，图形边缘的差异隐私引入了扰动图邻近性的噪音，这是图形对比度学习中的图形增强。受到的启发，我们建议利用图形对比学习的优势，以减轻差异隐私引起的性能下降。广泛的实验是通过几种代表性的图形模型和广泛使用的数据集进行的，表明对比度学习确实减轻了由差异隐私引起的模型的性能下降。

对于人工智能系统来说，在低计算成本的情况下实现准确的视频识别是一项挑战。基于自适应推理的有效视频识别方法通常会预览视频，并专注于显着零件以降低计算成本。大多数现有作品都集中在复杂的网络学习，并具有基于视频分类的目标。以所有框架为正样本，其中很少有人关注积极样本（显着框架）和负面样本（非空位框架）之间的歧视。为了填补这一空白，在本文中，我们提出了一个新型的非高度抑制网络（NSNET），该网络有效地抑制了非征力框架的响应。具体而言，在框架级别上，可以生成可以区分显着框架和非空位框架的有效伪标签，以指导框架显着性学习。在视频层面上，在双重视频级别的监督下都学会了一个时间关注模块，这些模块既是对突出表示和非偏心表示形式。从两个两个级别的显着度测量都合并以利用多粒性互补信息。在四个众所周知的基准上进行的广泛实验验证了我们的NSNET不仅实现了最先进的准确性效率折衷，而且比最先进的推理速度要快得多（2.4〜4.3倍） - 艺术方法。我们的项目页面位于https://lawrencexia2008.github.io/projects/nsnet。