OpenRaiser/Intern-Atlas huggingface dataset:
Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification
Venue: arXiv cs.CV 2024
Year: 2024

Precise aircraft recognition in low-resolution remote sensing imagery is a challenging yet crucial task in aviation, especially combat identification. This research addresses this problem with a novel, scalable, and AI-driven solution. The primary hurdle in combat identification in remote sensing imagery is the accurate recognition of Novel/Unknown types of aircraft in addition to Known types. Traditional methods, human expert-driven combat identification and image classification, fall short in identifying Novel classes. Our methodology employs similarity learning to discern features of a broad spectrum of military and civilian aircraft. It discerns both Known and Novel aircraft types, leveraging metric learning for the identification and supervised few-shot learning for aircraft type classification. To counter the challenge of limited low-resolution remote sensing data, we propose an end-to-end framework that adapts to the diverse and versatile process of military aircraft recognition by training a generalized embedder in fully supervised manner. Comparative analysis with earlier aircraft image classification methods shows that our approach is effective for aircraft image classification (F1-score Aircraft Type of 0.861) and pioneering for quantifying the identification of Novel types (F1-score Bipartitioning of 0.936). The proposed methodology effectively addresses inherent challenges in remote sensing data, thereby setting new standards in dataset quality. The research opens new avenues for domain experts and demonstrates unique capabilities in distinguishing various aircraft types, contributing to a more robust, domain-adapted potential for real-time aircraft recognition.
Memory GAPS: Would LLMs pass the Tulving Test?
Venue: arXiv cs.AI 2024
Year: 2024

The Tulving Test was designed to investigate memory performance in recognition and recall tasks. Its results help assess the relevance of the "Synergistic Ecphory Model" of memory and similar RK paradigms in human performance. This paper starts investigating whether the more than forty-year-old framework sheds some light on LLMs' acts of remembering.
Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking
Venue: arXiv cs.CV 2024
Year: 2024

Manually-designed network architectures for thermal infrared pedestrian tracking (TIR-PT) require substantial effort from human experts. AlexNet and ResNet are widely used as backbone networks in TIR-PT applications. However, these architectures were originally designed for image classification and object detection tasks, which are less complex than the challenges presented by TIR-PT. This paper makes an early attempt to search an optimal network architecture for TIR-PT automatically, employing single-bottom and dual-bottom cells as basic search units and incorporating eight operation candidates within the search space. To expedite the search process, a random channel selection strategy is employed prior to assessing operation candidates. Classification, batch hard triplet, and center loss are jointly used to retrain the searched architecture. The outcome is a high-performance network architecture that is both parameter- and computation-efficient. Extensive experiments proved the effectiveness of the automated method.
PAQA: Toward ProActive Open-Retrieval Question Answering
Venue: arXiv cs.CL 2024
Year: 2024

Conversational systems have made significant progress in generating natural language responses. However, their potential as conversational search systems is currently limited due to their passive role in the information-seeking process. One major limitation is the scarcity of datasets that provide labelled ambiguous questions along with a supporting corpus of documents and relevant clarifying questions. This work aims to tackle the challenge of generating relevant clarifying questions by taking into account the inherent ambiguities present in both user queries and documents. To achieve this, we propose PAQA, an extension to the existing AmbiNQ dataset, incorporating clarifying questions. We then evaluate various models and assess how passage retrieval impacts ambiguity detection and the generation of clarifying questions. By addressing this gap in conversational search systems, we aim to provide additional supervision to enhance their active participation in the information-seeking process and provide users with more accurate results.
Contextualized Diffusion Models for Text-Guided Image and Video Generation
Venue: arXiv cs.CV 2024
Year: 2024

Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion models primarily focus on incorporating text-visual relationships exclusively into the reverse process, often disregarding their relevance in the forward process. This inconsistency between forward and reverse processes may limit the precise conveyance of textual semantics in visual synthesis results. To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes. We propagate this context to all timesteps in the two processes to adapt their trajectories, thereby facilitating cross-modal conditional modeling. We generalize our contextualized diffusion to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing. In each task, our ContextDiff achieves new state-of-the-art performance, significantly enhancing the semantic alignment between text condition and generated samples, as evidenced by quantitative and qualitative evaluations. Our code is available at https://github.com/YangLing0818/ContextDiff
Domain Embeddings for Generating Complex Descriptions of Concepts in Italian Language
Venue: arXiv cs.CL 2024
Year: 2024

In this work, we propose a Distributional Semantic resource enriched with linguistic and lexical information extracted from electronic dictionaries, designed to address the challenge of bridging the gap between the continuous semantic values represented by distributional vectors and the discrete descriptions offered by general semantics theory. Recently, many researchers have concentrated on the nexus between embeddings and a comprehensive theory of semantics and meaning. This often involves decoding the representation of word meanings in Distributional Models into a set of discrete, manually constructed properties such as semantic primitives or features, using neural decoding techniques. Our approach introduces an alternative strategy grounded in linguistic data. We have developed a collection of domain-specific co-occurrence matrices, derived from two sources: a classification of Italian nouns categorized into 4 semantic traits and 20 concrete noun sub-categories, and a list of Italian verbs classified according to their semantic classes. In these matrices, the co-occurrence values for each word are calculated exclusively with a defined set of words pertinent to a particular lexical domain. The resource comprises 21 domain-specific matrices, one comprehensive matrix, and a Graphical User Interface. Our model facilitates the generation of reasoned semantic descriptions of concepts by selecting matrices directly associated with concrete conceptual knowledge, such as a matrix based on location nouns and the concept of animal habitats. We assessed the utility of the resource through two experiments, achieving promising outcomes in both: the automatic classification of animal nouns and the extraction of animal features.
BOXREC: Recommending a Box of Preferred Outfits in Online Shopping
Venue: arXiv cs.IR 2024
Year: 2024

Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user's fashion taste. However, none of these consider user's preference of price-range for individual clothing types or an overall shopping budget for a set of items. In this paper, we propose a box recommendation framework - BOXREC - which at first, collects user preferences across different item types (namely, top-wear, bottom-wear and foot-wear) including price-range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price-ranges), creates all possible combinations of three preferred items (belonging to distinct item types) and verifies each combination using an outfit scoring framework - BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. Empirical results show superior performance of BOXREC-OSF over the baseline methods.
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Venue: arXiv cs.CL 2024
Year: 2024

Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging. However, their utilization in the domain of code documentation generation remains underexplored. To this end, we introduce RepoAgent, a large language model powered open-source framework aimed at proactively generating, maintaining, and updating code documentation. Through both qualitative and quantitative evaluations, we have validated the effectiveness of our approach, showing that RepoAgent excels in generating high-quality repository-level documentation. The code and results are publicly accessible at https://github.com/OpenBMB/RepoAgent.
Program-Based Strategy Induction for Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.
Adaptation of Biomedical and Clinical Pretrained Models to French Long Documents: A Comparative Study
Venue: arXiv cs.CL 2024
Year: 2024

Recently, pretrained language models based on BERT have been introduced for the French biomedical domain. Although these models have achieved state-of-the-art results on biomedical and clinical NLP tasks, they are constrained by a limited input sequence length of 512 tokens, which poses challenges when applied to clinical notes. In this paper, we present a comparative study of three adaptation strategies for long-sequence models, leveraging the Longformer architecture. We conducted evaluations of these models on 16 downstream tasks spanning both biomedical and clinical domains. Our findings reveal that further pre-training an English clinical model with French biomedical texts can outperform both converting a French biomedical BERT to the Longformer architecture and pre-training a French biomedical Longformer from scratch. The results underscore that long-sequence French biomedical models improve performance across most downstream tasks regardless of sequence length, but BERT based models remain the most efficient for named entity recognition tasks.
Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)
Venue: arXiv cs.RO 2024
Year: 2024

Real-world autonomous driving (AD) especially urban driving involves many corner cases. The lately released AD simulator CARLA v2 adds 39 common events in the driving scene, and provide more quasi-realistic testbed compared to CARLA v1. It poses new challenge to the community and so far no literature has reported any success on the new scenarios in V2 as existing works mostly have to rely on specific rules for planning yet they cannot cover the more complex cases in CARLA v2. In this work, we take the initiative of directly training a planner and the hope is to handle the corner cases flexibly and effectively, which we believe is also the future of AD. To our best knowledge, we develop the first model-based RL method named Think2Drive for AD, with a world model to learn the transitions of the environment, and then it acts as a neural simulator to train the planner. This paradigm significantly boosts the training efficiency due to the low dimensional state space and parallel computing of tensors in the world model. As a result, Think2Drive is able to run in an expert-level proficiency in CARLA v2 within 3 days of training on a single A6000 GPU, and to our best knowledge, so far there is no reported success (100\% route completion)on CARLA v2. We also propose CornerCase-Repository, a benchmark that supports the evaluation of driving models by scenarios. Additionally, we propose a new and balanced metric to evaluate the performance by route completion, infraction number, and scenario density, so that the driving score could give more information about the actual driving performance.
Multichain Taprootized Atomic Swaps: Introducing Untraceability through Zero-Knowledge Proofs
Venue: arXiv cs.CR 2024
Year: 2024

Taprootized Atomic Swaps is an extension for Atomic Swaps that enables the untraceability of transactions in a particular swap. Based on Schnorr signatures, Taproot technology, and zero-knowledge proofs, the taprootized atomic swaps hide swap transactions between regular payments. We propose several implementation options: single-transaction protocol, multiple-transaction protocol that splits the receiving amount in an untraceable way, and multichain swap protocol. Our proposed approach works with any smart-contract-compatible chain and multiple Taproot-compatible chains. We describe the concrete implementation of the protocol and release the source code publically.
The Paradox of Industrial Involvement in Engineering Higher Education
Venue: arXiv cs.CY 2024
Year: 2024

This paper discusses the importance of reflective and socially conscious education in engineering schools, particularly within the EE/CS sector. While most engineering disciplines have historically aligned themselves with the demands of the technology industry, the lack of critical examination of industry practices and their impact on justice, equality, and sustainability is self-evident. Today, the for-profit engineering/technology companies, some of which are among the largest in the world, also shape the narrative of engineering education and research in universities. As engineering graduates form the largest cohorts within STEM disciplines in Western countries, they become future professionals who will work, lead, or even establish companies in this industry. Unfortunately, the curriculum within engineering education often lacks a deep understanding of social realities, an essential component of a comprehensive university education. Here we establish this unusual connection with the industry that has driven engineering higher education for several decades and its obvious negative impacts to society. We analyse this nexus and highlight the need for engineering schools to hold a more critical viewpoint. Given the wealth and power of modern technology companies, particularly in the ICT domain, questioning their techno-solutionism narrative is essential within the institutes of higher education.
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Venue: arXiv cs.IR 2024
Year: 2024

Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents from trustworthy corpora, e.g., Wikipedia, to produce specific answers. Very recently, a pre-trained generative retrieval model for KILTs, named CorpusBrain, was proposed and reached new state-of-the-art retrieval performance. However, most existing research on KILTs, including CorpusBrain, has predominantly focused on a static document collection, overlooking the dynamic nature of real-world scenarios, where new documents are continuously being incorporated into the source corpus. To address this gap, it is crucial to explore the capability of retrieval models to effectively handle the dynamic retrieval scenario inherent in KILTs. In this work, we first introduce the continual document learning (CDL) task for KILTs and build a novel benchmark dataset named KILT++ based on the original KILT dataset for evaluation. Then, we conduct a comprehensive study over the use of pre-trained CorpusBrain on KILT++. Unlike the promising results in the stationary scenario, CorpusBrain is prone to catastrophic forgetting in the dynamic scenario, hence hampering the retrieval performance. To alleviate this issue, we propose CorpusBrain++, a continual generative pre-training framework. Empirical results demonstrate the significant effectiveness and remarkable efficiency of CorpusBrain++ in comparison to both traditional and generative IR methods.
Video-Based Autism Detection with Deep Learning
Venue: arXiv cs.CV 2024
Year: 2024

Individuals with Autism Spectrum Disorder (ASD) often experience challenges in health, communication, and sensory processing; therefore, early diagnosis is necessary for proper treatment and care. In this work, we consider the problem of detecting or classifying ASD children to aid medical professionals in early diagnosis. We develop a deep learning model that analyzes video clips of children reacting to sensory stimuli, with the intent of capturing key differences in reactions and behavior between ASD and non-ASD participants. Unlike many recent studies in ASD classification with MRI data, which require expensive specialized equipment, our method utilizes a powerful but relatively affordable GPU, a standard computer setup, and a video camera for inference. Results show that our model effectively generalizes and understands key differences in the distinct movements of the children. It is noteworthy that our model exhibits successful classification performance despite the limited amount of data for a deep learning problem and limited temporal information available for learning, even with the motion artifacts.
Multi-Human Mesh Recovery with Transformers
Venue: arXiv cs.CV 2024
Year: 2024

Conventional approaches to human mesh recovery predominantly employ a region-based strategy. This involves initially cropping out a human-centered region as a preprocessing step, with subsequent modeling focused on this zoomed-in image. While effective for single figures, this pipeline poses challenges when dealing with images featuring multiple individuals, as different people are processed separately, often leading to inaccuracies in relative positioning. Despite the advantages of adopting a whole-image-based approach to address this limitation, early efforts in this direction have fallen short in performance compared to recent region-based methods. In this work, we advocate for this under-explored area of modeling all people at once, emphasizing its potential for improved accuracy in multi-person scenarios through considering all individuals simultaneously and leveraging the overall context and interactions. We introduce a new model with a streamlined transformer-based design, featuring three critical design choices: multi-scale feature incorporation, focused attention mechanisms, and relative joint supervision. Our proposed model demonstrates a significant performance improvement, surpassing state-of-the-art region-based and whole-image-based methods on various benchmarks involving multiple individuals.
Investigating the Effectiveness of HyperTuning via Gisting
Venue: arXiv cs.CL 2024
Year: 2024

Gisting (Mu et al., 2023) is a simple method for training models to compress information into fewer token representations using a modified attention mask, and can serve as an economical approach to training Transformer-based hypernetworks. We introduce HyperLlama, a set of Gisting-based hypernetworks built on Llama-2 models that generates task-specific soft prefixes based on few-shot inputs. In experiments across P3, Super-NaturalInstructions and Symbol Tuning datasets, we show that HyperLlama models can effectively compress information from few-shot examples into soft prefixes. However, they still underperform multi-task fine-tuned language models with full attention over few-shot in-context examples. We also show that HyperLlama-generated soft prefixes can serve as better initializations for further prefix tuning. Overall, Gisting-based hypernetworks are economical and easy to implement, but have mixed empirical performance.
Pragmatic Goal-Oriented Communications under Semantic-Effectiveness Channel Errors
Venue: arXiv cs.IT 2024
Year: 2024

In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This integration will enable sensing, transmission, and processing of exclusively pertinent task data, ensuring conveyed information possesses understandable, pragmatic semantic significance, aligning with destination needs and goals. Without doubt, no communication is error free. Within this context, besides errors stemming from typical wireless communication dynamics, potential distortions between transmitter-intended and receiver-interpreted meanings can emerge due to limitations in semantic processing capabilities, as well as language and knowledge representation disparities between transmitters and receivers. The main contribution of this paper is two-fold. First, it proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. Second, it provides a novel algorithmic solution to counteract these types of errors which leverages optimal transport theory. Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.