Detecting Generated Images by Real Images Only

As deep learning technology continues to evolve, the images yielded by generative models are becoming more and more realistic, triggering people to question the authenticity of images. Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This learning paradigm will result in efficiency and generalization issues, making detection methods always lag behind generation methods. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace. As a result, images from different generative models can be detected, solving some long-existing problems in the field. Experimental results show that although our method was trained only by real images and uses 99.9\% less training data than other deep learning-based methods, it can compete with state-of-the-art methods and shows excellent performance in detecting emerging generative models with high inference efficiency. Moreover, the proposed method shows robustness against various post-processing. These advantages allow the method to be used in real-world scenarios.

Grasp Multiple Objects with One Hand

The human hand's complex kinematics allow for simultaneous grasping and manipulation of multiple objects, essential for tasks like object transfer and in-hand manipulation. Despite its importance, robotic multi-object grasping remains underexplored and presents challenges in kinematics, dynamics, and object configurations. This paper introduces MultiGrasp, a two-stage method for multi-object grasping on a tabletop with a multi-finger dexterous hand. It involves (i) generating pre-grasp proposals and (ii) executing the grasp and lifting the objects. Experimental results primarily focus on dual-object grasping and report a 44.13% success rate, showcasing adaptability to unseen object configurations and imprecise grasps. The framework also demonstrates the capability to grasp more than two objects, albeit at a reduced inference speed.

Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce

In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics, including NDCG, customer click-through rates, and human assessments, to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.

Towards LLM-driven Dialogue State Tracking

Dialogue State Tracking (DST) is of paramount importance in ensuring accurate tracking of user goals and system actions within task-oriented dialogue systems. The emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. In this study, we conduct an initial examination of ChatGPT's capabilities in DST. Our evaluation uncovers the exceptional performance of ChatGPT in this task, offering valuable insights to researchers regarding its capabilities and providing useful directions for designing and enhancing dialogue systems. Despite its impressive performance, ChatGPT has significant limitations including its closed-source nature, request restrictions, raising data privacy concerns, and lacking local deployment capabilities. To address these concerns, we present LDST, an LLM-driven DST framework based on smaller, open-source foundation models. By utilizing a novel domain-slot instruction tuning method, LDST achieves performance on par with ChatGPT. Comprehensive evaluations across three distinct experimental settings, we find that LDST exhibits remarkable performance improvements in both zero-shot and few-shot setting compared to previous SOTA methods. The source code is provided for reproducibility.

Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking

The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. This definition is based on an assumption of group consensus consistency that is not always reasonable in the open-world setting, which results in robustness issue in the model when dealing with irrelevant images in the inputting image group under the open-word scenarios. To tackle this problem, we introduce a group selective exchange-masking (GSEM) approach for enhancing the robustness of the CoSOD model. GSEM takes two groups of images as input, each containing different types of salient objects. Based on the mixed metric we designed, GSEM selects a subset of images from each group using a novel learning-based strategy, then the selected images are exchanged. To simultaneously consider the uncertainty introduced by irrelevant images and the consensus features of the remaining relevant images in the group, we designed a latent variable generator branch and CoSOD transformer branch. The former is composed of a vector quantised-variational autoencoder to generate stochastic global variables that model uncertainty. The latter is designed to capture correlation-based local features that include group consensus. Finally, the outputs of the two branches are merged and passed to a transformer-based decoder to generate robust predictions. Taking into account that there are currently no benchmark datasets specifically designed for open-world scenarios, we constructed three open-world benchmark datasets, namely OWCoSal, OWCoSOD, and OWCoCA, based on existing datasets. By breaking the group-consistency assumption, these datasets provide effective simulations of real-world scenarios and can better evaluate the robustness and practicality of models.

Making Multimodal Generation Easier: When Diffusion Models Meet LLMs

We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs). Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge the gap between modalities, EasyGen is built upon a bidirectional conditional diffusion model named BiDiffuser, which promotes more efficient interactions between modalities. EasyGen handles image-to-text generation by integrating BiDiffuser and an LLM via a simple projection layer. Unlike most existing multimodal models that are limited to generating text responses, EasyGen can also facilitate text-to-image generation by leveraging the LLM to create textual descriptions, which can be interpreted by BiDiffuser to generate appropriate visual responses. Extensive quantitative and qualitative experiments demonstrate the effectiveness of EasyGen, whose training can be easily achieved in a lab setting. The source code is available at https://github.com/zxy556677/EasyGen.

Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts

Lion (Evolved Sign Momentum), a new optimizer discovered through program search, has shown promising results in training large AI models. It performs comparably or favorably to AdamW but with greater memory efficiency. As we can expect from the results of a random search program, Lion incorporates elements from several existing algorithms, including signed momentum, decoupled weight decay, Polak, and Nesterov momentum, but does not fit into any existing category of theoretically grounded optimizers. Thus, even though Lion appears to perform well as a general-purpose optimizer for a wide range of tasks, its theoretical basis remains uncertain. This lack of theoretical clarity limits opportunities to further enhance and expand Lion's efficacy. This work aims to demystify Lion. Based on both continuous-time and discrete-time analysis, we demonstrate that Lion is a theoretically novel and principled approach for minimizing a general loss function $f(x)$ while enforcing a bound constraint $\|x\|_\infty \leq 1/\lambda$. Lion achieves this through the incorporation of decoupled weight decay, where $\lambda$ represents the weight decay coefficient. Our analysis is made possible by the development of a new Lyapunov function for the Lion updates. It applies to a broader family of Lion-$\kappa$ algorithms, where the $\text{sign}(\cdot)$ operator in Lion is replaced by the subgradient of a convex function $\kappa$, leading to the solution of a general composite optimization problem of $\min_x f(x) + \kappa^*(x)$. Our findings provide valuable insights into the dynamics of Lion and pave the way for further improvements and extensions of Lion-related algorithms.

RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiratory motion limits its accuracy. Recently, 3D imaging from single X-ray projection receives extensive attentions as a promising way to address this issue. However, current methods can only reconstruct 3D image without direct location of the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirement of motion control in radiotherapy. In this study, we propose a novel imaging method RT-SRTS which integrates 3D imaging and tumor segmentation into one network based on the multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection at any angle. Futhermore, we propose the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules to aid feature extraction and improve segmentation accuracy. We evaluated the proposed method on ten patient cases and compared it with two state-of-the-art methods. Our approach not only delivered superior 3D reconstruction but also demonstrated commendable tumor segmentation results. The simultaneous reconstruction and segmentation could be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacy of both AEC and URE was also validated through ablation studies.

UCF-Crime Annotation: A Benchmark for Surveillance Video-and-Language Understanding

Surveillance videos are an essential component of daily life with various critical applications, particularly in public security. However, current surveillance video tasks mainly focus on classifying and localizing anomalous events. Existing methods are limited to detecting and classifying the predefined events with unsatisfactory generalization ability and semantic understanding, although they have obtained considerable performance. To address this issue, we propose constructing the first multimodal surveillance video dataset by manually annotating the real-world surveillance dataset UCF-Crime with fine-grained event content and timing. Our newly annotated dataset, UCA (UCF-Crime Annotation), provides a novel benchmark for multimodal surveillance video analysis. It not only describes events in detailed descriptions but also provides precise temporal grounding of the events in 0.1-second intervals. UCA contains 20,822 sentences, with an average length of 23 words, and its annotated videos are as long as 102 hours. Furthermore, we benchmark the state-of-the-art models of multiple multimodal tasks on this newly created dataset, including temporal sentence grounding in videos, video captioning, and dense video captioning. Through our experiments, we found that mainstream models used in previously publicly available datasets perform poorly on multimodal surveillance video scenarios, which highlights the necessity of constructing this dataset. The link to our dataset and code is provided at: https://github.com/Xuange923/UCA-dataset.