Hyperspectral video (HSV) offers valuable spatial, spectral, and temporal information simultaneously, making it highly suitable for handling challenges such as background clutter and visual similarity in object tracking. However, existing methods primarily focus on band regrouping and rely on RGB trackers for feature extraction, resulting in limited exploration of spectral information and difficulties in achieving complementary representations of object features. In this paper, a spatial-spectral fusion network with spectral angle awareness (SST-Net) is proposed for hyperspectral (HS) object tracking. Firstly, to address the issue of insufficient spectral feature extraction in existing networks, a spatial-spectral feature backbone ($S^2$FB) is designed. With the spatial and spectral extraction branch, a joint representation of texture and spectrum is obtained. Secondly, a spectral attention fusion module (SAFM) is presented to capture the intra- and inter-modality correlation to obtain the fused features from the HS and RGB modalities. It can incorporate the visual information into the HS spectral context to form a robust representation. Thirdly, to ensure a more accurate response of the tracker to the object position, a spectral angle awareness module (SAAM) investigates the region-level spectral similarity between the template and search images during the prediction stage. Furthermore, we develop a novel spectral angle awareness loss (SAAL) to offer guidance for the SAAM based on similar regions. Finally, to obtain the robust tracking results, a weighted prediction method is considered to combine the HS and RGB predicted motions of objects to leverage the strengths of each modality. Extensive experiments on the HOTC dataset demonstrate the effectiveness of the proposed SSF-Net, compared with state-of-the-art trackers.
Due to the rapid generation and dissemination of information, large language models (LLMs) quickly run out of date despite enormous development costs. Due to this crucial need to keep models updated, online learning has emerged as a critical necessity when utilizing LLMs for real-world applications. However, given the ever-expanding corpus of unseen documents and the large parameter space of modern LLMs, efficient adaptation is essential. To address these challenges, we propose Memory of Amortized Contexts (MAC), an efficient and effective online adaptation framework for LLMs with strong knowledge retention. We propose an amortized feature extraction and memory-augmentation approach to compress and extract information from new documents into compact modulations stored in a memory bank. When answering questions, our model attends to and extracts relevant knowledge from this memory bank. To learn informative modulations in an efficient manner, we utilize amortization-based meta-learning, which substitutes the optimization process with a single forward pass of the encoder. Subsequently, we learn to choose from and aggregate selected documents into a single modulation by conditioning on the question, allowing us to adapt a frozen language model during test time without requiring further gradient updates. Our experiment demonstrates the superiority of MAC in multiple aspects, including online adaptation performance, time, and memory efficiency. Code is available at: https://github.com/jihoontack/MAC.
In this study, we investigate the potential of GPT-4 and its advanced iteration, GPT-4 Turbo, in autonomously developing a detailed entity type taxonomy. Our objective is to construct a comprehensive taxonomy, starting from a broad classification of entity types - including objects, time, locations, organizations, events, actions, and subjects - similar to existing manually curated taxonomies. This classification is then progressively refined through iterative prompting techniques, leveraging GPT-4's internal knowledge base. The result is an extensive taxonomy comprising over 5000 nuanced entity types, which demonstrates remarkable quality upon subjective evaluation. We employed a straightforward yet effective prompting strategy, enabling the taxonomy to be dynamically expanded. The practical applications of this detailed taxonomy are diverse and significant. It facilitates the creation of new, more intricate branches through pattern-based combinations and notably enhances information extraction tasks, such as relation extraction and event argument extraction. Our methodology not only introduces an innovative approach to taxonomy creation but also opens new avenues for applying such taxonomies in various computational linguistics and AI-related fields.
The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-carrying orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we measure the ranges of scattered paths induced by targets, through the extraction of time-delay information from the received signals at APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering that the scattered paths corresponding to some targets may not be detectable by all APs, we propose an effective algorithm to match the range measurements with the targets and achieve the target location estimation. Notably, by analyzing the OFDM numerologies defined in fifth generation (5G) standards, we elucidate the flexibility and consistency of performance trade-offs in both communication and sensing aspects. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.
Process of information extraction (IE) is often used to extract meaningful information from unstructured and unlabeled data. Conventional methods of data extraction including application of OCR and passing extraction engine, are inefficient on large data and have their limitation. In this paper, a peculiar technique of information extraction is proposed using A2I and computer vision technologies, which also includes NLP.
Recently developed pre-trained text-and-layout models (PTLMs) have shown remarkable success in multiple information extraction tasks on visually-rich documents. However, the prevailing evaluation pipeline may not be sufficiently robust for assessing the information extraction ability of PTLMs, due to inadequate annotations within the benchmarks. Therefore, we claim the necessary standards for an ideal benchmark to evaluate the information extraction ability of PTLMs. We then introduce EC-FUNSD, an entity-centric benckmark designed for the evaluation of semantic entity recognition and entity linking on visually-rich documents. This dataset contains diverse formats of document layouts and annotations of semantic-driven entities and their relations. Moreover, this dataset disentangles the falsely coupled annotation of segment and entity that arises from the block-level annotation of FUNSD. Experiment results demonstrate that state-of-the-art PTLMs exhibit overfitting tendencies on the prevailing benchmarks, as their performance sharply decrease when the dataset bias is removed.
Named Entity Recognition (NER) is a sequence classification Natural Language Processing task where entities are identified in the text and classified into predefined categories. It acts as a foundation for most information extraction systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with its own diverse lore. DnD entities are domain-specific and are thus unrecognizable by even the state-of-the-art off-the-shelf NER systems as the NER systems are trained on general data for pre-defined categories such as: person (PERS), location (LOC), organization (ORG), and miscellaneous (MISC). For meaningful extraction of information from fantasy text, the entities need to be classified into domain-specific entity categories as well as the models be fine-tuned on a domain-relevant corpus. This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework that uses a pre-trained model for NER. Upon this training, the system acquires the ability to extract monster names from relevant domain documents under a novel NER tag. This work compares the accuracy of the monster name identification against; the zero-shot Trankit model and two FLAIR models. The fine-tuned Trankit model achieves an 87.86% F1 score surpassing all the other considered models.
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance. We replace the self-attention mechanism with a combination of global-local filter blocks to optimize model efficiency. The global filters extract features from the whole feature map whereas the local filters are being adaptively created as 4x4 patches of the same feature map and add restricted scale information. In particular, the feature extraction takes place in the frequency domain rather than the commonly used spatial (image) domain to facilitate faster computations. The fusion of information from both spatial and frequency spaces creates an efficient model with regards to complexity, required data and performance. We test GLFNet on three benchmark datasets achieving state-of-the-art performance on all of them while being almost twice as efficient in terms of GFLOP operations.
Precision in identifying and differentiating micro and macro blood vessels in the retina is crucial for the diagnosis of retinal diseases, although it poses a significant challenge. Current autoencoding-based segmentation approaches encounter limitations as they are constrained by the encoder and undergo a reduction in resolution during the encoding stage. The inability to recover lost information in the decoding phase further impedes these approaches. Consequently, their capacity to extract the retinal microvascular structure is restricted. To address this issue, we introduce Swin-Res-Net, a specialized module designed to enhance the precision of retinal vessel segmentation. Swin-Res-Net utilizes the Swin transformer which uses shifted windows with displacement for partitioning, to reduce network complexity and accelerate model convergence. Additionally, the model incorporates interactive fusion with a functional module in the Res2Net architecture. The Res2Net leverages multi-scale techniques to enlarge the receptive field of the convolutional kernel, enabling the extraction of additional semantic information from the image. This combination creates a new module that enhances the localization and separation of micro vessels in the retina. To improve the efficiency of processing vascular information, we've added a module to eliminate redundant information between the encoding and decoding steps. Our proposed architecture produces outstanding results, either meeting or surpassing those of other published models. The AUC reflects significant enhancements, achieving values of 0.9956, 0.9931, and 0.9946 in pixel-wise segmentation of retinal vessels across three widely utilized datasets: CHASE-DB1, DRIVE, and STARE, respectively. Moreover, Swin-Res-Net outperforms alternative architectures, demonstrating superior performance in both IOU and F1 measure metrics.
In the rapidly evolving field of scientific research, efficiently extracting key information from the burgeoning volume of scientific papers remains a formidable challenge. This paper introduces an innovative framework designed to automate the extraction of vital data from scientific PDF documents, enabling researchers to discern future research trajectories more readily. AutoIE uniquely integrates four novel components: (1) A multi-semantic feature fusion-based approach for PDF document layout analysis; (2) Advanced functional block recognition in scientific texts; (3) A synergistic technique for extracting and correlating information on molecular sieve synthesis; (4) An online learning paradigm tailored for molecular sieve literature. Our SBERT model achieves high Marco F1 scores of 87.19 and 89.65 on CoNLL04 and ADE datasets. In addition, a practical application of AutoIE in the petrochemical molecular sieve synthesis domain demonstrates its efficacy, evidenced by an impressive 78\% accuracy rate. This research paves the way for enhanced data management and interpretation in molecular sieve synthesis. It is a valuable asset for seasoned experts and newcomers in this specialized field.