OpenRaiser/Intern-Atlas huggingface dataset:
Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification
Venue: arXiv cs.CV 2024
Year: 2024

This work proposes a novel method for object co-segmentation, i.e. pixel-level localization of a common object in a set of images, that uses no pixel-level supervision for training. Two pre-trained Vision Transformer (ViT) models are exploited: ImageNet classification-trained ViT, whose features are used to estimate rough object localization through intra-class token relevance, and a self-supervised DINO-ViT for intra-image token relevance. On recent challenging benchmarks, the method achieves state-of-the-art performance among methods trained with the same level of supervision (image labels) while being competitive with methods trained with pixel-level supervision (binary masks). The benefits of the proposed co-segmentation method are further demonstrated in the task of large-scale sketch recognition, that is, the classification of sketches into a wide range of categories. The limited amount of hand-drawn sketch training data is leveraged by exploiting readily available image-level-annotated datasets of natural images containing a large number of classes. To bridge the domain gap, the classifier is trained on a sketch-like proxy domain derived from edges detected on natural images. We show that sketch recognition significantly benefits when the classifier is trained on sketch-like structures extracted from the co-segmented area rather than from the full image. Code: https://github.com/nikosips/CBNC .
Let Me Finish My Sentence: Video Temporal Grounding with Holistic Text Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Video Temporal Grounding (VTG) aims to identify visual frames in a video clip that match text queries. Recent studies in VTG employ cross-attention to correlate visual frames and text queries as individual token sequences. However, these approaches overlook a crucial aspect of the problem: a holistic understanding of the query sentence. A model may capture correlations between individual word tokens and arbitrary visual frames while possibly missing out on the global meaning. To address this, we introduce two primary contributions: (1) a visual frame-level gate mechanism that incorporates holistic textual information, (2) cross-modal alignment loss to learn the fine-grained correlation between query and relevant frames. As a result, we regularize the effect of individual word tokens and suppress irrelevant visual frames. We demonstrate that our method outperforms state-of-the-art approaches in VTG benchmarks, indicating that holistic text understanding guides the model to focus on the semantically important parts within the video.
Normalizing self-supervised learning for provably reliable Change Point Detection
Venue: arXiv cs.LG 2024
Year: 2024

Change point detection (CPD) methods aim to identify abrupt shifts in the distribution of input data streams. Accurate estimators for this task are crucial across various real-world scenarios. Yet, traditional unsupervised CPD techniques face significant limitations, often relying on strong assumptions or suffering from low expressive power due to inherent model simplicity. In contrast, representation learning methods overcome these drawbacks by offering flexibility and the ability to capture the full complexity of the data without imposing restrictive assumptions. However, these approaches are still emerging in the CPD field and lack robust theoretical foundations to ensure their reliability. Our work addresses this gap by integrating the expressive power of representation learning with the groundedness of traditional CPD techniques. We adopt spectral normalization (SN) for deep representation learning in CPD tasks and prove that the embeddings after SN are highly informative for CPD. Our method significantly outperforms current state-of-the-art methods during the comprehensive evaluation via three standard CPD datasets.
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
Venue: arXiv cs.CL 2024
Year: 2024

Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields diminishing performance improvements and heavy computational costs. Recently, OpenAI's o1 model has shown that inference strategies (i.e., Test-time Compute methods) can also significantly enhance the reasoning capabilities of LLMs. However, the mechanisms behind these methods are still unexplored. In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i.e., math, coding, commonsense reasoning). Specifically, first, our experiments show that the o1 model has achieved the best performance on most datasets. Second, as for the methods of searching diverse responses (e.g., BoN), we find the reward models' capability and the search space both limit the upper boundary of these methods. Third, as for the methods that break the problem into many sub-problems, the Agent Workflow has achieved better performance than Step-wise BoN due to the domain-specific system prompt for planning better reasoning processes. Fourth, it is worth mentioning that we have summarized six reasoning patterns of o1, and provided a detailed analysis on several reasoning benchmarks.
Label-free prediction of fluorescence markers in bovine satellite cells using deep learning
Venue: arXiv cs.CV 2024
Year: 2024

Assessing the quality of bovine satellite cells (BSCs) is essential for the cultivated meat industry, which aims to address global food sustainability challenges. This study aims to develop a label-free method for predicting fluorescence markers in isolated BSCs using deep learning. We employed a U-Net-based CNN model to predict multiple fluorescence signals from a single bright-field microscopy image of cell culture. Two key biomarkers, DAPI and Pax7, were used to determine the abundance and quality of BSCs. The image pre-processing pipeline included fluorescence denoising to improve prediction performance and consistency. A total of 48 biological replicates were used, with statistical performance metrics such as Pearson correlation coefficient and SSIM employed for model evaluation. The model exhibited better performance with DAPI predictions due to uniform staining. Pax7 predictions were more variable, reflecting biological heterogeneity. Enhanced visualization techniques, including color mapping and image overlay, improved the interpretability of the predictions by providing better contextual and perceptual information. The findings highlight the importance of data pre-processing and demonstrate the potential of deep learning to advance non-invasive, label-free assessment techniques in the cultivated meat industry, paving the way for reliable and actionable AI-driven evaluations.
Jailbreaking LLM-Controlled Robots
Venue: arXiv cs.RO 2024
Year: 2024

The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When viewed as a stand-alone technology, LLMs are known to be vulnerable to jailbreaking attacks, wherein malicious prompters elicit harmful text by bypassing LLM safety guardrails. To assess the risks of deploying LLMs in robotics, in this paper, we introduce RoboPAIR, the first algorithm designed to jailbreak LLM-controlled robots. Unlike existing, textual attacks on LLM chatbots, RoboPAIR elicits harmful physical actions from LLM-controlled robots, a phenomenon we experimentally demonstrate in three scenarios: (i) a white-box setting, wherein the attacker has full access to the NVIDIA Dolphins self-driving LLM, (ii) a gray-box setting, wherein the attacker has partial access to a Clearpath Robotics Jackal UGV robot equipped with a GPT-4o planner, and (iii) a black-box setting, wherein the attacker has only query access to the GPT-3.5-integrated Unitree Robotics Go2 robot dog. In each scenario and across three new datasets of harmful robotic actions, we demonstrate that RoboPAIR, as well as several static baselines, finds jailbreaks quickly and effectively, often achieving 100% attack success rates. Our results reveal, for the first time, that the risks of jailbroken LLMs extend far beyond text generation, given the distinct possibility that jailbroken robots could cause physical damage in the real world. Indeed, our results on the Unitree Go2 represent the first successful jailbreak of a deployed commercial robotic system. Addressing this emerging vulnerability is critical for ensuring the safe deployment of LLMs in robotics. Additional media is available at: https://robopair.org
Persistent Pre-Training Poisoning of LLMs
Venue: arXiv cs.CR 2024
Year: 2024

Large language models are pre-trained on uncurated text datasets consisting of trillions of tokens scraped from the Web. Prior work has shown that: (1) web-scraped pre-training datasets can be practically poisoned by malicious actors; and (2) adversaries can compromise language models after poisoning fine-tuning datasets. Our work evaluates for the first time whether language models can also be compromised during pre-training, with a focus on the persistence of pre-training attacks after models are fine-tuned as helpful and harmless chatbots (i.e., after SFT and DPO). We pre-train a series of LLMs from scratch to measure the impact of a potential poisoning adversary under four different attack objectives (denial-of-service, belief manipulation, jailbreaking, and prompt stealing), and across a wide range of model sizes (from 600M to 7B). Our main result is that poisoning only 0.1% of a model's pre-training dataset is sufficient for three out of four attacks to measurably persist through post-training. Moreover, simple attacks like denial-of-service persist through post-training with a poisoning rate of only 0.001%.
Interacting humans and robots can improve sensory prediction by adapting their viscoelasticity
Venue: arXiv cs.RO 2024
Year: 2024

To manipulate objects or dance together, humans and robots exchange energy and haptic information. While the exchange of energy in human-robot interaction has been extensively investigated, the underlying exchange of haptic information is not well understood. Here, we develop a computational model of the mechanical and sensory interactions between agents that can tune their viscoelasticity while considering their sensory and motor noise. The resulting stochastic-optimal-information-and-effort (SOIE) controller predicts how the exchange of haptic information and the performance can be improved by adjusting viscoelasticity. This controller was first implemented on a robot-robot experiment with a tracking task which showed its superior performance when compared to either stiff or compliant control. Importantly, the optimal controller also predicts how connected humans alter their muscle activation to improve haptic communication, with differentiated viscoelasticity adjustment to their own sensing noise and haptic perturbations. A human-robot experiment then illustrated the applicability of this optimal control strategy for robots, yielding improved tracking performance and effective haptic communication as the robot adjusted its viscoelasticity according to its own and the user's noise characteristics. The proposed SOIE controller may thus be used to improve haptic communication and collaboration of humans and robots.
A Pattern to Align Them All: Integrating Different Modalities to Define Multi-Modal Entities
Venue: arXiv cs.AI 2024
Year: 2024

The ability to reason with and integrate different sensory inputs is the foundation underpinning human intelligence and it is the reason for the growing interest in modelling multi-modal information within Knowledge Graphs. Multi-Modal Knowledge Graphs extend traditional Knowledge Graphs by associating an entity with its possible modal representations, including text, images, audio, and videos, all of which are used to convey the semantics of the entity. Despite the increasing attention that Multi-Modal Knowledge Graphs have received, there is a lack of consensus about the definitions and modelling of modalities, whose definition is often determined by application domains. In this paper, we propose a novel ontology design pattern that captures the separation of concerns between an entity (and the information it conveys), whose semantics can have different manifestations across different media, and its realisation in terms of a physical information entity. By introducing this abstract model, we aim to facilitate the harmonisation and integration of different existing multi-modal ontologies which is crucial for many intelligent applications across different domains spanning from medicine to digital humanities.
DreamVideo-2: Zero-Shot Subject-Driven Video Customization with Precise Motion Control
Venue: arXiv cs.CV 2024
Year: 2024

Recent advances in customized video generation have enabled users to create videos tailored to both specific subjects and motion trajectories. However, existing methods often require complicated test-time fine-tuning and struggle with balancing subject learning and motion control, limiting their real-world applications. In this paper, we present DreamVideo-2, a zero-shot video customization framework capable of generating videos with a specific subject and motion trajectory, guided by a single image and a bounding box sequence, respectively, and without the need for test-time fine-tuning. Specifically, we introduce reference attention, which leverages the model's inherent capabilities for subject learning, and devise a mask-guided motion module to achieve precise motion control by fully utilizing the robust motion signal of box masks derived from bounding boxes. While these two components achieve their intended functions, we empirically observe that motion control tends to dominate over subject learning. To address this, we propose two key designs: 1) the masked reference attention, which integrates a blended latent mask modeling scheme into reference attention to enhance subject representations at the desired positions, and 2) a reweighted diffusion loss, which differentiates the contributions of regions inside and outside the bounding boxes to ensure a balance between subject and motion control. Extensive experimental results on a newly curated dataset demonstrate that DreamVideo-2 outperforms state-of-the-art methods in both subject customization and motion control. The dataset, code, and models will be made publicly available.
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Venue: arXiv cs.CV 2024
Year: 2024

Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-image generation, focusing on two critical factors: whether models use discrete or continuous tokens, and whether tokens are generated in a random or fixed raster order using BERT- or GPT-like transformer architectures. Our empirical results show that, while all models scale effectively in terms of validation loss, their evaluation performance -- measured by FID, GenEval score, and visual quality -- follows different trends. Models based on continuous tokens achieve significantly better visual quality than those using discrete tokens. Furthermore, the generation order and attention mechanisms significantly affect the GenEval score: random-order models achieve notably better GenEval scores compared to raster-order models. Inspired by these findings, we train Fluid, a random-order autoregressive model on continuous tokens. Fluid 10.5B model achieves a new state-of-the-art zero-shot FID of 6.16 on MS-COCO 30K, and 0.69 overall score on the GenEval benchmark. We hope our findings and results will encourage future efforts to further bridge the scaling gap between vision and language models.
SpaceRaceEdu: developing an educational multi-player videogame for self-study and assessment
Venue: arXiv cs.CY 2024
Year: 2024

The teaching innovation project SpaceRaceEdu: development of an educational multiplayer video game for self-study and self-assessment has been carried out under the INNOVA call of the Autonomous University of Madrid during the 2022-2023 academic year. In this project, a functional prototype of SpaceRaceEdu has been developed: a multiplayer video game with a social and educational nature, which can be used both by teachers as a training and evaluation activity and by students as a tool for study and evaluation. In SpaceRaceEdu, several student teams try to launch a rocket before everyone else. To meet this objective, they must gather a series of resources by going through a scenario and answering questions of different types. The teachers can introduce these questions according to the contents of their subject. The videogame balances competition and cooperation to promote participation and learning. Competition occurs between teams who strive to answer all their questions correctly before their rivals. In contrast, cooperation occurs between students on the same team who can organize and support each other to be more effective.
"Efficient Complexity": a Constrained Optimization Approach to the Evolution of Natural Intelligence
Venue: arXiv cs.NE 2024
Year: 2024

A fundamental question in the conjunction of information theory, biophysics, bioinformatics and thermodynamics relates to the principles and processes that guide the development of natural intelligence in natural environments where information about external stimuli may not be available at prior. A novel approach in the description of the information processes of natural learning is proposed in the framework of constrained optimization, where the objective function represented by the information entropy of the internal states of the system with the states of the external environment is maximized under the natural constraints of memory, computing power, energy and other essential resources. The progress of natural intelligence can be interpreted in this framework as a strategy of approximation of the solutions of the optimization problem via a traversal over the extrema network of the objective function under the natural constraints that were examined and described. Non-trivial conclusions on the relationships between the complexity, variability and efficiency of the structure, or architecture of learning models made on the basis of the proposed formalism can explain the effectiveness of neural networks as collaborative groups of small intelligent units in biological and artificial intelligence.
Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, interest in vision-language tasks has grown, especially those involving chart interactions. These tasks are inherently multimodal, requiring models to process chart images, accompanying text, underlying data tables, and often user queries. Traditionally, Chart Understanding (CU) relied on heuristics and rule-based systems. However, recent advancements that have integrated transformer architectures significantly improved performance. This paper reviews prominent research in CU, focusing on State-of-The-Art (SoTA) frameworks that employ transformers within End-to-End (E2E) solutions. Relevant benchmarking datasets and evaluation techniques are analyzed. Additionally, this article identifies key challenges and outlines promising future directions for advancing CU solutions. Following the PRISMA guidelines, a comprehensive literature search is conducted across Google Scholar, focusing on publications from Jan'20 to Jun'24. After rigorous screening and quality assessment, 32 studies are selected for in-depth analysis. The CU tasks are categorized into a three-layered paradigm based on the cognitive task required. Recent advancements in the frameworks addressing various CU tasks are also reviewed. Frameworks are categorized into single-task or multi-task based on the number of tasks solvable by the E2E solution. Within multi-task frameworks, pre-trained and prompt-engineering-based techniques are explored. This review overviews leading architectures, datasets, and pre-training tasks. Despite significant progress, challenges remain in OCR dependency, handling low-resolution images, and enhancing visual reasoning. Future directions include addressing these challenges, developing robust benchmarks, and optimizing model efficiency. Additionally, integrating explainable AI techniques and exploring the balance between real and synthetic data are crucial for advancing CU research.
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation
Venue: arXiv cs.CR 2024
Year: 2024

As generative AI systems, including large language models (LLMs) and diffusion models, advance rapidly, their growing adoption has led to new and complex security risks often overlooked in traditional AI risk assessment frameworks. This paper introduces a novel formal framework for categorizing and mitigating these emergent security risks by integrating adaptive, real-time monitoring, and dynamic risk mitigation strategies tailored to generative models' unique vulnerabilities. We identify previously under-explored risks, including latent space exploitation, multi-modal cross-attack vectors, and feedback-loop-induced model degradation. Our framework employs a layered approach, incorporating anomaly detection, continuous red-teaming, and real-time adversarial simulation to mitigate these risks. We focus on formal verification methods to ensure model robustness and scalability in the face of evolving threats. Though theoretical, this work sets the stage for future empirical validation by establishing a detailed methodology and metrics for evaluating the performance of risk mitigation strategies in generative AI systems. This framework addresses existing gaps in AI safety, offering a comprehensive road map for future research and implementation.
Large Language Model-driven Multi-Agent Simulation for News Diffusion Under Different Network Structures
Venue: arXiv cs.SI 2024
Year: 2024

The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from conventional agent-based simulation approaches, this work introduces an innovative approach by employing a large language model (LLM)-driven multi-agent simulation to replicate complex interactions within information ecosystems. We investigate key factors that facilitate news propagation, such as agent personalities and network structures, while also evaluating strategies to combat misinformation. Through simulations across varying network structures, we demonstrate the potential of LLM-based agents in modeling the dynamics of misinformation spread, validating the influence of agent traits on the diffusion process. Our findings emphasize the advantages of LLM-based simulations over traditional techniques, as they uncover underlying causes of information spread -- such as agents promoting discussions -- beyond the predefined rules typically employed in existing agent-based models. Additionally, we evaluate three countermeasure strategies, discovering that brute-force blocking influential agents in the network or announcing news accuracy can effectively mitigate misinformation. However, their effectiveness is influenced by the network structure, highlighting the importance of considering network structure in the development of future misinformation countermeasures.