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Camera-Pose Robust Crater Detection from Chang’e 5

Camera-Pose Robust Crater Detection from Chang’e 5

arXiv:2406.04569v1 Announce Type: new Abstract: As space missions aim to explore increasingly hazardous terrain, accurate and timely position estimates are required to ensure safe navigation. Vision-based navigation achieves this goal through correlating impact craters visible through onboard imagery with a known database to estimate a craft's pose. However, existing literature has not sufficiently evaluated crater-detection algorithm (CDA) performance from imagery containing off-nadir view angles. In this work, we evaluate the performance of Mask R-CNN for crater detection, comparing models pretrained on simulated data containing off-nadir view angles and to pretraining on real-lunar images. We demonstrate pretraining on real-lunar images is…
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To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation

To Distill or Not to Distill? On the Robustness of Robust Knowledge Distillation

arXiv:2406.04512v1 Announce Type: new Abstract: Arabic is known to present unique challenges for Automatic Speech Recognition (ASR). On one hand, its rich linguistic diversity and wide range of dialects complicate the development of robust, inclusive models. On the other, current multilingual ASR models are compute-intensive and lack proper comprehensive evaluations. In light of these challenges, we distill knowledge from large teacher models into smaller student variants that are more efficient. We also introduce a novel human-annotated dataset covering five under-represented Arabic dialects for evaluation. We further evaluate both our models and existing SoTA multilingual models on both standard available benchmarks…
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User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU

User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU

[Submitted on 6 Jun 2024] View a PDF of the paper titled User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU, by Sakshi Mahendru View PDF Abstract:Query Processing (QP) is optimized by a Cloud-based cache by storing the frequently accessed data closer to users. Nevertheless, the lack of focus on user intention type in queries affected the efficiency of QP in prevailing works. Thus, by using a Contextual Fuzzy Linguistic Inference System (CFLIS), this work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. Primarily, the user query is parsed using tokenization,…
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Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance

Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance

[Submitted on 6 Jun 2024] View a PDF of the paper titled Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance, by Reyhane Askari Hemmat and 5 other authors View PDF HTML (experimental) Abstract:With the growing popularity of text-to-image generative models, there has been increasing focus on understanding their risks and biases. Recent work has found that state-of-the-art models struggle to depict everyday objects with the true diversity of the real world and have notable gaps between geographic regions. In this work, we aim to increase the diversity of generated images of common objects such that per-region variations are…
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Time Sensitive Knowledge Editing through Efficient Finetuning

Time Sensitive Knowledge Editing through Efficient Finetuning

arXiv:2406.04496v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. It is thus essential to design effective methods to both update obsolete knowledge and induce new knowledge into LLMs. Existing locate-and-edit knowledge editing (KE) method suffers from two limitations. First, the post-edit LLMs by such methods generally have poor capability in answering complex queries that require multi-hop reasoning. Second, the long run-time of such locate-and-edit methods to perform knowledge edits make it…
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Negative Feedback for Music Personalization

Negative Feedback for Music Personalization

arXiv:2406.04488v1 Announce Type: new Abstract: Next-item recommender systems are often trained using only positive feedback with randomly-sampled negative feedback. We show the benefits of using real negative feedback both as inputs into the user sequence and also as negative targets for training a next-song recommender system for internet radio. In particular, using explicit negative samples during training helps reduce training time by ~60% while also improving test accuracy by ~6%; adding user skips as additional inputs also can considerably increase user coverage alongside slightly improving accuracy. We test the impact of using a large number of random negative samples to…
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FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar

FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar

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Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs

Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs

arXiv:2406.04482v1 Announce Type: new Abstract: Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still lacking. To address this, we propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game DejaBoom!, our approach effectively identifies bugs inherent…
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A multi-core periphery perspective: Ranking via relative centrality

A multi-core periphery perspective: Ranking via relative centrality

[Submitted on 6 Jun 2024] View a PDF of the paper titled A multi-core periphery perspective: Ranking via relative centrality, by Chandra Sekhar Mukherjee and 1 other authors View PDF HTML (experimental) Abstract:Community and core-periphery are two widely studied graph structures, with their coexistence observed in real-world graphs (Rombach, Porter, Fowler & Mucha [SIAM J. App. Math. 2014, SIAM Review 2017]). However, the nature of this coexistence is not well understood and has been pointed out as an open problem (Yanchenko & Sengupta [Statistics Surveys, 2023]). Especially, the impact of inferring the core-periphery structure of a graph on understanding its…
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M&M VTO: Multi-Garment Virtual Try-On and Editing

M&M VTO: Multi-Garment Virtual Try-On and Editing

arXiv:2406.04542v1 Announce Type: new Abstract: We present M&M VTO, a mix and match virtual try-on method that takes as input multiple garment images, text description for garment layout and an image of a person. An example input includes: an image of a shirt, an image of a pair of pants, "rolled sleeves, shirt tucked in", and an image of a person. The output is a visualization of how those garments (in the desired layout) would look like on the given person. Key contributions of our method are: 1) a single stage diffusion based model, with no super resolution cascading, that…
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