arXiv:2506.21484v1 Announce Type: cross Abstract: We focus on the source-free domain adaptive object detection (SF-DAOD) problem when source data is unavailable during adaptation and the model must adapt to an unlabeled target domain. The majority of approaches for the problem employ a self-supervised approach using a student-teacher (ST) framework where pseudo-labels are generated via a […]
[Submitted on 2 Jun 2025 (v1), last revised 25 Jun 2025 (this version, v2)] View a PDF of the paper titled TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation, by Amin Karimi Monsefi and 5 other authors View PDF HTML (experimental) Abstract:We propose TaxaDiffusion, a taxonomy-informed training framework for diffusion models to generate fine-grained
arXiv:2506.20946v1 Announce Type: cross Abstract: Current texture synthesis methods, which generate textures from fixed viewpoints, suffer from inconsistencies due to the lack of global context and geometric understanding. Meanwhile, recent advancements in video generation models have demonstrated remarkable success in achieving temporally consistent videos. In this paper, we introduce VideoTex, a novel framework for seamless
[Submitted on 14 Sep 2024 (v1), last revised 26 Jun 2025 (this version, v2)] View a PDF of the paper titled Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models, by Alireza Salemi and 1 other authors View PDF HTML (experimental) Abstract:Despite its substantial impact on various search, recommendation, and question answering
[Submitted on 23 Jun 2025 (v1), last revised 26 Jun 2025 (this version, v2)] View a PDF of the paper titled IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection, by Abhay Kumar and 2 other authors View PDF HTML (experimental) Abstract:Advancements in audio deepfake technology offers benefits like AI assistants, better accessibility for speech
arXiv:2506.21368v1 Announce Type: cross Abstract: We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one
[Submitted on 29 Nov 2024 (v1), last revised 26 Jun 2025 (this version, v3)] View a PDF of the paper titled Pretrained Reversible Generation as Unsupervised Visual Representation Learning, by Rongkun Xue and 6 other authors View PDF HTML (experimental) Abstract:Recent generative models based on score matching and flow matching have significantly advanced generation tasks,
arXiv:2506.21288v1 Announce Type: cross Abstract: Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) tasks. However, LLMs struggle to answer queries reliably when the provided context lacks information, often resorting to ungrounded speculation or internal knowledge. Groundedness – generating responses strictly supported by the context – is
[Submitted on 13 Jun 2025 (v1), last revised 26 Jun 2025 (this version, v2)] View a PDF of the paper titled Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation, by Min-Seop Kwak and 6 other authors View PDF HTML (experimental) Abstract:We introduce a diffusion-based framework that performs aligned novel view image and
People often think ‘affordable’ housing is much more expensive than it actually is, and then conclude it’s a scam to make housing for rich people. But this is often based on a misunderstanding of how the prices are set. Let’s say a unit is “50% AMI” somewhere with an area median income (AMI) of $100k.
