Spatial-wise Dynamic Distillation for MLP-like Efficient Visual Fault Detection of Freight Trains

Despite the successful application of convolutional neural networks (CNNs) in object detection tasks, their efficiency in detecting faults from freight train images remains inadequate for implementation in real-world engineering scenarios. Existing modeling shortcomings of spatial invariance and pooling layers in conventional CNNs often ignore the neglect of crucial global information, resulting in error localization for fault objection tasks of freight trains. To solve these problems, we design a spatial-wise dynamic distillation framework based on multi-layer perceptron (MLP) for visual fault detection of freight trains. We initially present the axial shift strategy, which allows the MLP-like architecture to overcome the challenge of spatial invariance and effectively incorporate both local and global cues. We propose a dynamic distillation method without a pre-training teacher, including a dynamic teacher mechanism that can effectively eliminate the semantic discrepancy with the student model. Such an approach mines more abundant details from lower-level feature appearances and higher-level label semantics as the extra supervision signal, which utilizes efficient instance embedding to model the global spatial and semantic information. In addition, the proposed dynamic teacher can jointly train with students to further enhance the distillation efficiency. Extensive experiments executed on six typical fault datasets reveal that our approach outperforms the current state-of-the-art detectors and achieves the highest accuracy with real-time detection at a lower computational cost. The source code will be available at \url{https://github.com/MVME-HBUT/SDD-FTI-FDet}.

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, \textit{i.e.}, general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things

Video Internet of Things (VIoT) has shown full potential in collecting an unprecedented volume of video data. Learning to schedule perceiving models and analyzing the collected videos intelligently will be potential sparks for VIoT. In this paper, to address the challenges posed by the fine-grained and interrelated vision tool usage of VIoT, we build VIoTGPT, the framework based on LLMs to correctly interact with humans, query knowledge videos, and invoke vision models to accomplish complicated tasks. To support VIoTGPT and related future works, we meticulously crafted the training dataset and established benchmarks involving 11 representative vision models across three categories based on semi-automatic annotations. To guide LLM to act as the intelligent agent towards intelligent VIoT, we resort to ReAct instruction tuning based on the collected VIoT dataset to learn the tool capability. Quantitative and qualitative experimental results and analyses demonstrate the effectiveness of VIoTGPT.

Neural-Optic Co-Designed Polarization-Multiplexed Metalens for Compact Computational Spectral Imaging

As the realm of spectral imaging applications extends its reach into the domains of mobile technology and augmented reality, the demands for compact yet high-fidelity systems become increasingly pronounced. Conventional methodologies, exemplified by coded aperture snapshot spectral imaging systems, are significantly limited by their cumbersome physical dimensions and form factors. To address this inherent challenge, diffractive optical elements (DOEs) have been repeatedly employed as a means to mitigate issues related to the bulky nature of these systems. Nonetheless, it's essential to note that the capabilities of DOEs primarily revolve around the modulation of the phase of light. Here, we introduce an end-to-end computational spectral imaging framework based on a polarization-multiplexed metalens. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high-fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial-spectral reconstruction performance, underscoring the efficacy of this system in practical, real-world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.

Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling

Uncertainty decomposition refers to the task of decomposing the total uncertainty of a model into data (aleatoric) uncertainty, resulting from the inherent complexity or ambiguity of the data, and model (epistemic) uncertainty, resulting from the lack of knowledge in the model. Performing uncertainty decomposition for large language models (LLMs) is an important step toward improving the reliability, trustworthiness, and interpretability of LLMs, but this research task is very challenging and remains unresolved. The existing canonical method, Bayesian Neural Network (BNN), cannot be applied to LLMs, because BNN requires training and ensembling multiple variants of models, which is infeasible or prohibitively expensive for LLMs. In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarifications ensemble, which bypasses the need to train new models. Rather than ensembling models with different parameters, our approach generates a set of clarifications for the input, feeds them into the fixed LLMs, and ensembles the corresponding predictions. We show that our framework shares a symmetric decomposition structure with BNN. Empirical evaluations demonstrate that the proposed framework provides accurate and reliable uncertainty quantification on various tasks. Code will be made publicly available at https://github.com/UCSB-NLP-Chang/llm_uncertainty .

Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach

Image segmentation methods have been utilized to determine the particle size distribution of crushed ores. Due to the complex working environment, high-powered computing equipment is difficult to deploy. At the same time, the ore distribution is stacked, and it is difficult to identify the complete features. To address this issue, an effective box-supervised technique with texture features is provided for ore image segmentation that can identify complete and independent ores. Firstly, a ghost feature pyramid network (Ghost-FPN) is proposed to process the features obtained from the backbone to reduce redundant semantic information and computation generated by complex networks. Then, an optimized detection head is proposed to obtain the feature to maintain accuracy. Finally, Lab color space (Lab) and local binary patterns (LBP) texture features are combined to form a fusion feature similarity-based loss function to improve accuracy while incurring no loss. Experiments on MS COCO have shown that the proposed fusion features are also worth studying on other types of datasets. Extensive experimental results demonstrate the effectiveness of the proposed method, which achieves over 50 frames per second with a small model size of 21.6 MB. Meanwhile, the method maintains a high level of accuracy compared with the state-of-the-art approaches on ore image dataset. The source code is available at \url{https://github.com/MVME-HBUT/OREINST}.

Cross-modal Prominent Fragments Enhancement Aligning Network for Image-text Retrieval

Image-text retrieval is a widely studied topic in the field of computer vision due to the exponential growth of multimedia data, whose core concept is to measure the similarity between images and text. However, most existing retrieval methods heavily rely on cross-attention mechanisms for cross-modal fine-grained alignment, which takes into account excessive irrelevant regions and treats prominent and non-significant words equally, thereby limiting retrieval accuracy. This paper aims to investigate an alignment approach that reduces the involvement of non-significant fragments in images and text while enhancing the alignment of prominent segments. For this purpose, we introduce the Cross-Modal Prominent Fragments Enhancement Aligning Network(CPFEAN), which achieves improved retrieval accuracy by diminishing the participation of irrelevant regions during alignment and relatively increasing the alignment similarity of prominent words. Additionally, we incorporate prior textual information into image regions to reduce misalignment occurrences. In practice, we first design a novel intra-modal fragments relationship reasoning method, and subsequently employ our proposed alignment mechanism to compute the similarity between images and text. Extensive quantitative comparative experiments on MS-COCO and Flickr30K datasets demonstrate that our approach outperforms state-of-the-art methods by about 5% to 10% in the rSum metric.

Revitalizing Legacy Video Content: Deinterlacing with Bidirectional Information Propagation

Due to old CRT display technology and limited transmission bandwidth, early film and TV broadcasts commonly used interlaced scanning. This meant each field contained only half of the information. Since modern displays require full frames, this has spurred research into deinterlacing, i.e. restoring the missing information in legacy video content. In this paper, we present a deep-learning-based method for deinterlacing animated and live-action content. Our proposed method supports bidirectional spatio-temporal information propagation across multiple scales to leverage information in both space and time. More specifically, we design a Flow-guided Refinement Block (FRB) which performs feature refinement including alignment, fusion, and rectification. Additionally, our method can process multiple fields simultaneously, reducing per-frame processing time, and potentially enabling real-time processing. Our experimental results demonstrate that our proposed method achieves superior performance compared to existing methods.

CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text semantics, usually neglecting the valuable collaborative information from user-item interactions in recommendations. While these text-emphasizing approaches excel in cold-start scenarios, they may yield sub-optimal performance in warm-start situations. In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation. CoLLM captures collaborative information through an external traditional model and maps it to the input token embedding space of LLM, forming collaborative embeddings for LLM usage. Through this external integration of collaborative information, CoLLM ensures effective modeling of collaborative information without modifying the LLM itself, providing the flexibility to employ various collaborative information modeling techniques. Extensive experiments validate that CoLLM adeptly integrates collaborative information into LLMs, resulting in enhanced recommendation performance. We release the code and data at https://github.com/zyang1580/CoLLM.

Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models

Generative models have demonstrated revolutionary success in various visual creation tasks, but in the meantime, they have been exposed to the threat of leaking private information of their training data. Several membership inference attacks (MIAs) have been proposed to exhibit the privacy vulnerability of generative models by classifying a query image as a training dataset member or nonmember. However, these attacks suffer from major limitations, such as requiring shadow models and white-box access, and either ignoring or only focusing on the unique property of diffusion models, which block their generalization to multiple generative models. In contrast, we propose the first generalized membership inference attack against a variety of generative models such as generative adversarial networks, [variational] autoencoders, implicit functions, and the emerging diffusion models. We leverage only generated distributions from target generators and auxiliary non-member datasets, therefore regarding target generators as black boxes and agnostic to their architectures or application scenarios. Experiments validate that all the generative models are vulnerable to our attack. For instance, our work achieves attack AUC $>0.99$ against DDPM, DDIM, and FastDPM trained on CIFAR-10 and CelebA. And the attack against VQGAN, LDM (for the text-conditional generation), and LIIF achieves AUC $>0.90.$ As a result, we appeal to our community to be aware of such privacy leakage risks when designing and publishing generative models.