OpenRaiser/Intern-Atlas huggingface dataset:
Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding
Venue: arXiv cs.CV 2024
Year: 2024

As large-scale text-to-image generation models have made remarkable progress in the field of text-to-image generation, many fine-tuning methods have been proposed. However, these models often struggle with novel objects, especially with one-shot scenarios. Our proposed method aims to address the challenges of generalizability and fidelity in an object-driven way, using only a single input image and the object-specific regions of interest. To improve generalizability and mitigate overfitting, in our paradigm, a prototypical embedding is initialized based on the object's appearance and its class, before fine-tuning the diffusion model. And during fine-tuning, we propose a class-characterizing regularization to preserve prior knowledge of object classes. To further improve fidelity, we introduce object-specific loss, which can also use to implant multiple objects. Overall, our proposed object-driven method for implanting new objects can integrate seamlessly with existing concepts as well as with high fidelity and generalization. Our method outperforms several existing works. The code will be released.
Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings
Venue: arXiv cs.LG 2024
Year: 2024

The advancement of transformer neural networks has significantly elevated the capabilities of sentence similarity models, but they still struggle with highly discriminative tasks and may produce sub-optimal representations of important documents like scientific literature. With the increased reliance on retrieval augmentation and search, representing diverse documents as concise and descriptive vectors is crucial. This paper improves upon the vectors embeddings of scientific text by assembling niche datasets using co-citations as a similarity metric, focusing on biomedical domains. We apply a novel Mixture of Experts (MoE) extension pipeline to pretrained BERT models, where every multi-layer perceptron section is enlarged and copied into multiple distinct experts. Our MoE variants perform well over $N$ scientific domains with $N$ dedicated experts, whereas standard BERT models excel in only one domain at a time. Notably, extending just a single transformer block to MoE captures 85% of the benefit seen from full MoE extension at every layer. This holds promise for versatile and efficient One-Size-Fits-All transformer networks for numerically representing diverse inputs. Our methodology marks advancements in representation learning and holds promise for enhancing vector database search and compilation.
Exploring the Impact of Blockchain, AI, and ML on Financial Accounting Efficiency and Transformation
Venue: arXiv cs.CE 2024
Year: 2024

Continuous innovations profoundly impact the financial and commercial domains, reshaping conventional business practices. Among the disruptive forces, Artificial Intelligence (AI), Machine Learning (ML), and blockchain technology stand out prominently. This study aims to evaluate the integration of blockchain, AI, and ML within financial accounting practices. It suggests a potential revolutionary impact on financial accounting through the adoption of blockchain technology and ML, promising reduced accounting expenses, heightened precision, real-time financial reporting capabilities, and expeditious auditing processes. AI's role in automating repetitive financial accounting tasks assists organizations in circumventing the need for additional staff, thereby minimizing associated costs. Consequently, to bolster efficiency, businesses are increasingly embracing blockchain technology and AI applications in their financial accounting operations.
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks
Venue: arXiv cs.CV 2024
Year: 2024

Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient residual network, Efficient-ResNet. Attention-boosting gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to fuse the equivariant and feature-based semantic information with the equivalent sizes of the output of global context of the efficient residual network in the encoder. Respectively, the decoder network is developed with the additional attention-fusion networks (AfNs) inspired by AbM. AfNs are designed to improve the efficiency in the one-to-one conversion of the semantic information by deploying additional convolution layers in the decoder part. Our network is tested on the challenging CamVid and Cityscapes datasets, and the proposed methods reveal significant improvements on the residual networks. To the best of our knowledge, the developed network, SERNet-Former, achieves state-of-the-art results (84.62 % mean IoU) on CamVid dataset and challenging results (87.35 % mean IoU) on Cityscapes validation dataset.
An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion
Venue: arXiv cs.CV 2024
Year: 2024

Augmented reality for laparoscopic liver resection is a visualisation mode that allows a surgeon to localise tumours and vessels embedded within the liver by projecting them on top of a laparoscopic image. Preoperative 3D models extracted from CT or MRI data are registered to the intraoperative laparoscopic images during this process. In terms of 3D-2D fusion, most of the algorithms make use of anatomical landmarks to guide registration. These landmarks include the liver's inferior ridge, the falciform ligament, and the occluding contours. They are usually marked by hand in both the laparoscopic image and the 3D model, which is time-consuming and may contain errors if done by a non-experienced user. Therefore, there is a need to automate this process so that augmented reality can be used effectively in the operating room. We present the Preoperative-to-Intraoperative Laparoscopic Fusion Challenge (P2ILF), held during the Medical Imaging and Computer Assisted Interventions (MICCAI 2022) conference, which investigates the possibilities of detecting these landmarks automatically and using them in registration. The challenge was divided into two tasks: 1) A 2D and 3D landmark detection task and 2) a 3D-2D registration task. The teams were provided with training data consisting of 167 laparoscopic images and 9 preoperative 3D models from 9 patients, with the corresponding 2D and 3D landmark annotations. A total of 6 teams from 4 countries participated, whose proposed methods were evaluated on 16 images and two preoperative 3D models from two patients. All the teams proposed deep learning-based methods for the 2D and 3D landmark segmentation tasks and differentiable rendering-based methods for the registration task. Based on the experimental outcomes, we propose three key hypotheses that determine current limitations and future directions for research in this domain.
HRI Challenges Influencing Low Usage of Robotic Systems in Disaster Response and Rescue Operations
Venue: arXiv cs.RO 2024
Year: 2024

The breakthrough in AI and Machine Learning has brought a new revolution in robotics, resulting in the construction of more sophisticated robotic systems. Not only can these robotic systems benefit all domains, but also can accomplish tasks that seemed to be unimaginable a few years ago. From swarms of autonomous small robots working together to more very heavy and large objects, to seemingly indestructible robots capable of going to the harshest environments, we can see robotic systems designed for every task imaginable. Among them, a key scenario where robotic systems can benefit is in disaster response scenarios and rescue operations. Robotic systems are capable of successfully conducting tasks such as removing heavy materials, utilizing multiple advanced sensors for finding objects of interest, moving through debris and various inhospitable environments, and not the least have flying capabilities. Even with so much potential, we rarely see the utilization of robotic systems in disaster response scenarios and rescue missions. Many factors could be responsible for the low utilization of robotic systems in such scenarios. One of the key factors involve challenges related to Human-Robot Interaction (HRI) issues. Therefore, in this paper, we try to understand the HRI challenges involving the utilization of robotic systems in disaster response and rescue operations. Furthermore, we go through some of the proposed robotic systems designed for disaster response scenarios and identify the HRI challenges of those systems. Finally, we try to address the challenges by introducing ideas from various proposed research works.
Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text
Venue: arXiv cs.CL 2024
Year: 2024

The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing. These entities serve as essential building blocks for clinical information extraction, enabling retrieval of critical medical insights from vast amounts of textual data. Furthermore, the ability to identify and categorize these entities is fundamental for developing advanced clinical decision support systems, aiding healthcare professionals in diagnosis and treatment planning. In this study, we participated in SympTEMIST, a shared task on the detection of symptoms, signs and findings in Spanish medical documents. We combine a set of large language models fine-tuned with the data released by the organizers.
ARGOS: An Automaton Referencing Guided Overtake System for Head-to-Head Autonomous Racing
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous overtaking at high speeds is a challenging multi-agent robotics research problem. The high-speed and close proximity situations that arise in multi-agent autonomous racing require designing algorithms that trade off aggressive overtaking maneuvers and minimize the risk of collision with the opponent. In this paper, we study a special case of multi-agent autonomous race, called the head-to-head autonomous race, that requires two racecars with similar performance envelopes. We present a mathematical formulation of an overtake and position defense in this head-to-head autonomous racing scenario, and we introduce the Automaton Referencing Guided Overtake System (ARGOS) framework that supervises the execution of an overtake or position defense maneuver depending on the current role of the racecar. The ARGOS framework works by decomposing complex overtake and position-defense maneuvers into sequential and temporal submaneuvers that are individually managed and supervised by a network of automatons. We verify the properties of the ARGOS framework using model-checking and demonstrate results from multiple simulations, which show that the framework meets the desired specifications. The ARGOS framework performs similar to what can be observed from real-world human-driven motor sport racing.
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
Venue: arXiv cs.CV 2024
Year: 2024

The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation. Nowadays, the utilisation of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of convolutional neural networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The you only look once algorithmic framework is employed with traditional rectangular bounding boxes for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation.
Prediction of Breast Cancer Recurrence Risk Using a Multi-Model Approach Integrating Whole Slide Imaging and Clinicopathologic Features
Venue: arXiv cs.CV 2024
Year: 2024

Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. The Oncotype DX Breast Recurrence Score test is an important predictive and prognostic genomic assay for estrogen receptor-positive breast cancer that guides therapeutic strategies; however, such tests can be expensive, delay care, and are not widely available. The aim of this study was to develop a multi-model approach integrating the analysis of whole slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low and high risk. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. This method was trained and tested on 993 hematoxylin and eosin-stained whole-slide images of breast cancers with corresponding clinicopathological features that had prior Oncotype DX testing. The model's performance was evaluated using an internal test set of 198 patients from Dartmouth Health and an external test set of 418 patients from the University of Chicago. The multi-model approach achieved an AUC of 0.92 (95 percent CI: 0.88-0.96) on the internal set and an AUC of 0.85 (95 percent CI: 0.79-0.90) on the external cohort. These results suggest that with further validation, the proposed methodology could provide an alternative to assist clinicians in personalizing treatment for breast cancer patients and potentially improving their outcomes.
Swarm: Cost-Efficient Video Content Distribution with a Peer-to-Peer System
Venue: arXiv cs.NI 2024
Year: 2024

As ByteDance's business expands, the substantial infrastructure expenses associated with centralized Content Delivery Network (CDN) networks have rendered content distribution costs prohibitively high. In response, we embarked on exploring a peer-to-peer (P2P) network as a promising solution to alleviate the escalating costs of content distribution. However, the decentralized nature of P2P often introduces performance challenges, given the diversity and dispersion of peer devices. This study introduces Swarm, ByteDance's innovative hybrid system for video streaming. Swarm seamlessly integrates the robustness of a conventional CDN with the cost-efficiency of a decentralized P2P network. Its primary aim is to provide users with reliable streaming quality while minimizing traffic expenses. To achieve this, Swarm employs a centralized control plane comprised of a tracker cluster, overseeing a data plane with numerous edge residual resources. The tracker also takes on the responsibility of mapping clients to servers. Addressing the performance disparities among individual peer servers, Swarm utilizes our proprietary multipath parallel transmission method for communication between clients and peer servers. Operating stably for six years, Swarm now manages over a hundred thousand peer servers, serving nearly a hundred million users daily and saving the company hundreds of millions of RMB annually. Experimental results affirm that, while significantly cutting costs, Swarm performs on par with traditional CDNs.
2L3: Lifting Imperfect Generated 2D Images into Accurate 3D
Venue: arXiv cs.CV 2024
Year: 2024

Reconstructing 3D objects from a single image is an intriguing but challenging problem. One promising solution is to utilize multi-view (MV) 3D reconstruction to fuse generated MV images into consistent 3D objects. However, the generated images usually suffer from inconsistent lighting, misaligned geometry, and sparse views, leading to poor reconstruction quality. To cope with these problems, we present a novel 3D reconstruction framework that leverages intrinsic decomposition guidance, transient-mono prior guidance, and view augmentation to cope with the three issues, respectively. Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures. Our approach, therefore, enables the integration of a pre-trained MV image generator and a neural network-based volumetric signed distance function (SDF) representation for a single image to 3D object reconstruction. We evaluate our framework on various datasets and demonstrate its superior performance in both quantitative and qualitative assessments, signifying a significant advancement in 3D object reconstruction. Compared with the latest state-of-the-art method Syncdreamer~\cite{liu2023syncdreamer}, we reduce the Chamfer Distance error by about 36\% and improve PSNR by about 30\% .
Meta-Learning for Neural Network-based Temporal Point Processes
Venue: arXiv cs.LG 2024
Year: 2024

Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is widely used in many applications to predict such events related to human activities. However, point processes present two problems in predicting events related to human activities. First, recent high-performance point process models require the input of sufficient numbers of events collected over a long period (i.e., long sequences) for training, which are often unavailable in realistic situations. Second, the long-term predictions required in real-world applications are difficult. To tackle these problems, we propose a novel meta-learning approach for periodicity-aware prediction of future events given short sequences. The proposed method first embeds short sequences into hidden representations (i.e., task representations) via recurrent neural networks for creating predictions from short sequences. It then models the intensity of the point process by monotonic neural networks (MNNs), with the input being the task representations. We transfer the prior knowledge learned from related tasks and can improve event prediction given short sequences of target tasks. We design the MNNs to explicitly take temporal periodic patterns into account, contributing to improved long-term prediction performance. Experiments on multiple real-world datasets demonstrate that the proposed method has higher prediction performance than existing alternatives.
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression
Venue: arXiv cs.CV 2024
Year: 2024

Video compression performance is closely related to the accuracy of inter prediction. It tends to be difficult to obtain accurate inter prediction for the local video regions with inconsistent motion and occlusion. Traditional video coding standards propose various technologies to handle motion inconsistency and occlusion, such as recursive partitions, geometric partitions, and long-term references. However, existing learned video compression schemes focus on obtaining an overall minimized prediction error averaged over all regions while ignoring the motion inconsistency and occlusion in local regions. In this paper, we propose a spatial decomposition and temporal fusion based inter prediction for learned video compression. To handle motion inconsistency, we propose to decompose the video into structure and detail (SDD) components first. Then we perform SDD-based motion estimation and SDD-based temporal context mining for the structure and detail components to generate short-term temporal contexts. To handle occlusion, we propose to propagate long-term temporal contexts by recurrently accumulating the temporal information of each historical reference feature and fuse them with short-term temporal contexts. With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction. Comprehensive experimental results demonstrate that our codec outperforms the reference software of H.266/VVC on all common test datasets for both PSNR and MS-SSIM.
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
Venue: arXiv cs.DC 2024
Year: 2024

Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated learning framework. Specifically, during each communication round, we group clients into multiple clusters based on their model training status and data distribution on the server side. We then consider each cluster center as a node equipped with model parameters and construct a graph that connects these nodes using weighted edges. Additionally, we update the model parameters at each node by propagating information across the entire graph. Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side. We conduct experiments on three image benchmark datasets and create synthetic structured datasets with three types of typologies. Experimental results demonstrate the effectiveness of the proposed work.
Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach
Venue: arXiv cs.CV 2024
Year: 2024

Accurate and timely crop mapping is essential for yield estimation, insurance claims, and conservation efforts. Over the years, many successful machine learning models for crop mapping have been developed that use just the multi-spectral imagery from satellites to predict crop type over the area of interest. However, these traditional methods do not account for the physical processes that govern crop growth. At a high level, crop growth can be envisioned as physical parameters, such as weather and soil type, acting upon the plant leading to crop growth which can be observed via satellites. In this paper, we propose Weather-based Spatio-Temporal segmentation network with ATTention (WSTATT), a deep learning model that leverages this understanding of crop growth by formulating it as an inverse model that combines weather (Daymet) and satellite imagery (Sentinel-2) to generate accurate crop maps. We show that our approach provides significant improvements over existing algorithms that solely rely on spectral imagery by comparing segmentation maps and F1 classification scores. Furthermore, effective use of attention in WSTATT architecture enables detection of crop types earlier in the season (up to 5 months in advance), which is very useful for improving food supply projections. We finally discuss the impact of weather by correlating our results with crop phenology to show that WSTATT is able to capture physical properties of crop growth.