OpenRaiser/Intern-Atlas huggingface dataset:
Opening the Black Box of 3D Reconstruction Error Analysis with VECTOR
Venue: arXiv cs.CV 2024
Year: 2024

Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.
Hierarchical learning control for autonomous robots inspired by central nervous system
Venue: arXiv cs.RO 2024
Year: 2024

Mammals can generate autonomous behaviors in various complex environments through the coordination and interaction of activities at different levels of their central nervous system. In this paper, we propose a novel hierarchical learning control framework by mimicking the hierarchical structure of the central nervous system along with their coordination and interaction behaviors. The framework combines the active and passive control systems to improve both the flexibility and reliability of the control system as well as to achieve more diverse autonomous behaviors of robots. Specifically, the framework has a backbone of independent neural network controllers at different levels and takes a three-level dual descending pathway structure, inspired from the functionality of the cerebral cortex, cerebellum, and spinal cord. We comprehensively validated the proposed approach through the simulation as well as the experiment of a hexapod robot in various complex environments, including obstacle crossing and rapid recovery after partial damage. This study reveals the principle that governs the autonomous behavior in the central nervous system and demonstrates the effectiveness of the hierarchical control approach with the salient features of the hierarchical learning control architecture and combination of active and passive control systems.
Minimum Enclosing Ball Synthetic Minority Oversampling Technique from a Geometric Perspective
Venue: arXiv cs.LG 2024
Year: 2024

Class imbalance refers to the significant difference in the number of samples from different classes within a dataset, making it challenging to identify minority class samples correctly. This issue is prevalent in real-world classification tasks, such as software defect prediction, medical diagnosis, and fraud detection. The synthetic minority oversampling technique (SMOTE) is widely used to address class imbalance issue, which is based on interpolation between randomly selected minority class samples and their neighbors. However, traditional SMOTE and most of its variants only interpolate between existing samples, which may be affected by noise samples in some cases and synthesize samples that lack diversity. To overcome these shortcomings, this paper proposes the Minimum Enclosing Ball SMOTE (MEB-SMOTE) method from a geometry perspective. Specifically, MEB is innovatively introduced into the oversampling method to construct a representative point. Then, high-quality samples are synthesized by interpolation between this representative point and the existing samples. The rationale behind constructing a representative point is discussed, demonstrating that the center of MEB is more suitable as the representative point. To exhibit the superiority of MEB-SMOTE, experiments are conducted on 15 real-world imbalanced datasets. The results indicate that MEB-SMOTE can effectively improve the classification performance on imbalanced datasets.
Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

We primarily focus on the field of large language models (LLMs) for recommendation, which has been actively explored recently and poses a significant challenge in effectively enhancing recommender systems with logical reasoning abilities and open-world knowledge. Current mainstream efforts mainly center around injecting personalized information from recommendation models into LLMs by customizing input templates or aligning representations between semantic and recommendation spaces at the prediction layer. However, they face three significant limitations: (1) LoRA is mostly used as a core component in existing works, but personalization is not well established in LoRA parameters as the LoRA matrix shared by every user may not cater to different users' characteristics, leading to suboptimal performance. (2) Although lifelong personalized behavior sequences are ideal for personalization, their use raises effectiveness and efficiency issues since LLMs require escalating training and inference time to extend text lengths. (3) Existing approaches aren't scalable for large datasets due to training efficiency constraints. Thus, LLMs only see a small fraction of the datasets (e.g., less than 10%) instead of the whole datasets, limiting their exposure to the full training space. To address these problems, we propose RecLoRA. This model incorporates a Personalized LoRA module that maintains independent LoRAs for different users and a Long-Short Modality Retriever that retrieves different history lengths for different modalities, significantly improving performance while adding minimal time cost. Furthermore, we design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces. Extensive experiments on public datasets demonstrate the efficacy of our RecLoRA compared to existing baseline models.
PoseMamba: Monocular 3D Human Pose Estimation with Bidirectional Global-Local Spatio-Temporal State Space Model
Venue: arXiv cs.CV 2024
Year: 2024

Transformers have significantly advanced the field of 3D human pose estimation (HPE). However, existing transformer-based methods primarily use self-attention mechanisms for spatio-temporal modeling, leading to a quadratic complexity, unidirectional modeling of spatio-temporal relationships, and insufficient learning of spatial-temporal correlations. Recently, the Mamba architecture, utilizing the state space model (SSM), has exhibited superior long-range modeling capabilities in a variety of vision tasks with linear complexity. In this paper, we propose PoseMamba, a novel purely SSM-based approach with linear complexity for 3D human pose estimation in monocular video. Specifically, we propose a bidirectional global-local spatio-temporal SSM block that comprehensively models human joint relations within individual frames as well as temporal correlations across frames. Within this bidirectional global-local spatio-temporal SSM block, we introduce a reordering strategy to enhance the local modeling capability of the SSM. This strategy provides a more logical geometric scanning order and integrates it with the global SSM, resulting in a combined global-local spatial scan. We have quantitatively and qualitatively evaluated our approach using two benchmark datasets: Human3.6M and MPI-INF-3DHP. Extensive experiments demonstrate that PoseMamba achieves state-of-the-art performance on both datasets while maintaining a smaller model size and reducing computational costs. The code and models will be released.
D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods
Venue: arXiv cs.CV 2024
Year: 2024

In image processing, one of the most challenging tasks is to render an image's semantic meaning using a variety of artistic approaches. Existing techniques for arbitrary style transfer (AST) frequently experience mode-collapse, over-stylization, or under-stylization due to a disparity between the style and content images. We propose a novel framework called D$^2$Styler (Discrete Diffusion Styler) that leverages the discrete representational capability of VQ-GANs and the advantages of discrete diffusion, including stable training and avoidance of mode collapse. Our method uses Adaptive Instance Normalization (AdaIN) features as a context guide for the reverse diffusion process. This makes it easy to move features from the style image to the content image without bias. The proposed method substantially enhances the visual quality of style-transferred images, allowing the combination of content and style in a visually appealing manner. We take style images from the WikiArt dataset and content images from the COCO dataset. Experimental results demonstrate that D$^2$Styler produces high-quality style-transferred images and outperforms twelve existing methods on nearly all the metrics. The qualitative results and ablation studies provide further insights into the efficacy of our technique. The code is available at https://github.com/Onkarsus13/D2Styler.
MPC-Minimized Secure LLM Inference
Venue: arXiv cs.CR 2024
Year: 2024

Many inference services based on large language models (LLMs) pose a privacy concern, either revealing user prompts to the service or the proprietary weights to the user. Secure inference offers a solution to this problem through secure multi-party computation (MPC), however, it is still impractical for modern LLM workload due to the large overhead imposed by MPC. To address this overhead, we propose Marill, a framework that adapts LLM fine-tuning to minimize MPC usage during secure inference. Marill introduces high-level architectural changes during fine-tuning that significantly reduce the number of expensive operations needed within MPC during inference, by removing some and relocating others outside MPC without compromising security. As a result, Marill-generated models are more efficient across all secure inference protocols and our approach complements MPC-friendly approximations for such operations. Compared to standard fine-tuning, Marill results in 3.6-11.3x better runtime and 2.4-6.9x better communication during secure inference across various MPC settings, while typically preserving over 90% performance across downstream tasks.
Unraveling Privacy Threat Modeling Complexity: Conceptual Privacy Analysis Layers
Venue: arXiv cs.CR 2024
Year: 2024

Analyzing privacy threats in software products is an essential part of software development to ensure systems are privacy-respecting; yet it is still a far from trivial activity. While there have been many advancements in the past decade, they tend to focus on describing 'what' the threats are. What isn't entirely clear yet is 'how' to actually find these threats. Privacy is a complex domain. We propose to use four conceptual layers (feature, ecosystem, business context, and environment) to capture this privacy complexity. These layers can be used as a frame to structure and specify the privacy analysis support in a more tangible and actionable way, thereby improving applicability of the analysis process.
JARViS: Detecting Actions in Video Using Unified Actor-Scene Context Relation Modeling
Venue: arXiv cs.CV 2024
Year: 2024

Video action detection (VAD) is a formidable vision task that involves the localization and classification of actions within the spatial and temporal dimensions of a video clip. Among the myriad VAD architectures, two-stage VAD methods utilize a pre-trained person detector to extract the region of interest features, subsequently employing these features for action detection. However, the performance of two-stage VAD methods has been limited as they depend solely on localized actor features to infer action semantics. In this study, we propose a new two-stage VAD framework called Joint Actor-scene context Relation modeling based on Visual Semantics (JARViS), which effectively consolidates cross-modal action semantics distributed globally across spatial and temporal dimensions using Transformer attention. JARViS employs a person detector to produce densely sampled actor features from a keyframe. Concurrently, it uses a video backbone to create spatio-temporal scene features from a video clip. Finally, the fine-grained interactions between actors and scenes are modeled through a Unified Action-Scene Context Transformer to directly output the final set of actions in parallel. Our experimental results demonstrate that JARViS outperforms existing methods by significant margins and achieves state-of-the-art performance on three popular VAD datasets, including AVA, UCF101-24, and JHMDB51-21.
Improving the quality of Persian clinical text with a novel spelling correction system
Venue: arXiv cs.CL 2024
Year: 2024

Background: The accuracy of spelling in Electronic Health Records (EHRs) is a critical factor for efficient clinical care, research, and ensuring patient safety. The Persian language, with its abundant vocabulary and complex characteristics, poses unique challenges for real-word error correction. This research aimed to develop an innovative approach for detecting and correcting spelling errors in Persian clinical text. Methods: Our strategy employs a state-of-the-art pre-trained model that has been meticulously fine-tuned specifically for the task of spelling correction in the Persian clinical domain. This model is complemented by an innovative orthographic similarity matching algorithm, PERTO, which uses visual similarity of characters for ranking correction candidates. Results: The evaluation of our approach demonstrated its robustness and precision in detecting and rectifying word errors in Persian clinical text. In terms of non-word error correction, our model achieved an F1-Score of 90.0% when the PERTO algorithm was employed. For real-word error detection, our model demonstrated its highest performance, achieving an F1-Score of 90.6%. Furthermore, the model reached its highest F1-Score of 91.5% for real-word error correction when the PERTO algorithm was employed. Conclusions: Despite certain limitations, our method represents a substantial advancement in the field of spelling error detection and correction for Persian clinical text. By effectively addressing the unique challenges posed by the Persian language, our approach paves the way for more accurate and efficient clinical documentation, contributing to improved patient care and safety. Future research could explore its use in other areas of the Persian medical domain, enhancing its impact and utility.
TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization
Venue: arXiv cs.CV 2024
Year: 2024

We present TALE, a novel training-free framework harnessing the generative capabilities of text-to-image diffusion models to address the cross-domain image composition task that focuses on flawlessly incorporating user-specified objects into a designated visual contexts regardless of domain disparity. Previous methods often involve either training auxiliary networks or finetuning diffusion models on customized datasets, which are expensive and may undermine the robust textual and visual priors of pre-trained diffusion models. Some recent works attempt to break the barrier by proposing training-free workarounds that rely on manipulating attention maps to tame the denoising process implicitly. However, composing via attention maps does not necessarily yield desired compositional outcomes. These approaches could only retain some semantic information and usually fall short in preserving identity characteristics of input objects or exhibit limited background-object style adaptation in generated images. In contrast, TALE is a novel method that operates directly on latent space to provide explicit and effective guidance for the composition process to resolve these problems. Specifically, we equip TALE with two mechanisms dubbed Adaptive Latent Manipulation and Energy-guided Latent Optimization. The former formulates noisy latents conducive to initiating and steering the composition process by directly leveraging background and foreground latents at corresponding timesteps, and the latter exploits designated energy functions to further optimize intermediate latents conforming to specific conditions that complement the former to generate desired final results. Our experiments demonstrate that TALE surpasses prior baselines and attains state-of-the-art performance in image-guided composition across various photorealistic and artistic domains.
Towards Multimodal Emotional Support Conversation Systems
Venue: arXiv cs.MM 2024
Year: 2024

The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth and nuance of human conversations. Despite the potential, the current landscape of conversational AI is markedly limited by its reliance on single-modal data, constraining the systems' ability to empathize and provide effective emotional support. This limitation stems from a paucity of resources that encapsulate the multimodal nature of human communication essential for therapeutic counseling. To address this gap, we introduce the Multimodal Emotional Support Conversation (MESC) dataset, a first-of-its-kind resource enriched with comprehensive annotations across text, audio, and video modalities. This dataset captures the intricate interplay of user emotions, system strategies, system emotion, and system responses, setting a new precedent in the field. Leveraging the MESC dataset, we propose a general Sequential Multimodal Emotional Support framework (SMES) grounded in Therapeutic Skills Theory. Tailored for multimodal dialogue systems, the SMES framework incorporates an LLM-based reasoning model that sequentially generates user emotion recognition, system strategy prediction, system emotion prediction, and response generation. Our rigorous evaluations demonstrate that this framework significantly enhances the capability of AI systems to mimic therapist behaviors with heightened empathy and strategic responsiveness. By integrating multimodal data in this innovative manner, we bridge the critical gap between emotion recognition and emotional support, marking a significant advancement in conversational AI for mental health support.
AI-Driven approach for sustainable extraction of earth's subsurface renewable energy while minimizing seismic activity
Venue: arXiv cs.LG 2024
Year: 2024

Deep Geothermal Energy, Carbon Capture and Storage, and Hydrogen Storage hold considerable promise for meeting the energy sector's large-scale requirements and reducing CO$_2$ emissions. However, the injection of fluids into the Earth's crust, essential for these activities, can induce or trigger earthquakes. In this paper, we highlight a new approach based on Reinforcement Learning for the control of human-induced seismicity in the highly complex environment of an underground reservoir. This complex system poses significant challenges in the control design due to parameter uncertainties and unmodeled dynamics. We show that the reinforcement learning algorithm can interact efficiently with a robust controller, by choosing the controller parameters in real-time, reducing human-induced seismicity and allowing the consideration of further production objectives, \textit{e.g.}, minimal control power. Simulations are presented for a simplified underground reservoir under various energy demand scenarios, demonstrating the reliability and effectiveness of the proposed control-reinforcement learning approach.
Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model
Venue: arXiv cs.CV 2024
Year: 2024

In challenging low light and adverse weather conditions,thermal vision algorithms,especially object detection,have exhibited remarkable potential,contrasting with the frequent struggles encountered by visible vision algorithms. Nevertheless,the efficacy of thermal vision algorithms driven by deep learning models remains constrained by the paucity of available training data samples. To this end,this paper introduces a novel approach termed the edge guided conditional diffusion model. This framework aims to produce meticulously aligned pseudo thermal images at the pixel level,leveraging edge information extracted from visible images. By utilizing edges as contextual cues from the visible domain,the diffusion model achieves meticulous control over the delineation of objects within the generated images. To alleviate the impacts of those visible-specific edge information that should not appear in the thermal domain,a two-stage modality adversarial training strategy is proposed to filter them out from the generated images by differentiating the visible and thermal modality. Extensive experiments on LLVIP demonstrate ECDM s superiority over existing state-of-the-art approaches in terms of image generation quality.
'Finance Wizard' at the FinLLM Challenge Task: Financial Text Summarization
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents our participation under the team name `Finance Wizard' in the FinNLP-AgentScen 2024 shared task #2: Financial Text Summarization. It documents our pipeline approach of fine-tuning a foundation model into a task-specific model for Financial Text Summarization. It involves (1) adapting Llama3 8B, a foundation model, to the Finance domain via continued pre-training, (2) multi-task instruction-tuning to further equip the model with more finance-related capabilities, (3) finally fine-tuning the model into a task-specific `expert'. Our model, FinLlama3\_sum, yielded commendable results, securing the third position in its category with a ROUGE-1 score of 0.521.
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). It accommodates a diverse range of models, including both open-weight and API-based ones, and features over 35 safety benchmarks covering areas such as multilingual safety, exaggerated safety, and prompt injections. The framework supports both LLM and judge benchmarking and incorporates custom mutators to test safety against various text-style mutations, such as future tense and paraphrasing. Additionally, WalledEval introduces WalledGuard, a new, small, and performant content moderation tool, and two datasets: SGXSTest and HIXSTest, which serve as benchmarks for assessing the exaggerated safety of LLMs and judges in cultural contexts. We make WalledEval publicly available at https://github.com/walledai/walledeval.