Knowledge catalogue
arXiv:2606.22369v1 Announce Type: cross Abstract: In this paper, we present Kiwano, an open-source toolkit designed to advance research and evaluation for speaker verification. Kiwano provides a light
arXiv:2606.22189v1 Announce Type: new Abstract: Small language models are cheap to serve and feasible on local hardware, but strong public 135M-class systems are commonly trained with hundreds of bill
arXiv:2606.22634v1 Announce Type: new Abstract: Learning Entropy (LE) has recently been extended to image analysis through Spatial Learning Entropy Maps (SLEMs), which are two-dimensional LE distribut
arXiv:2606.20705v1 Announce Type: new Abstract: We ask whether the representational hierarchy seen in perception, from local primitives such as edges to higher level structures such as parts and objec
arXiv:2606.22377v1 Announce Type: new Abstract: Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D gri
arXiv:2606.20823v1 Announce Type: new Abstract: Facial landmark localisation is a prerequisite for developing automated, non-contact neonatal pain assessment methods. Clinicians use pain scales to jud
arXiv:2606.22002v1 Announce Type: new Abstract: Training medical image classifiers on entire datasets is wasteful when annotation budgets are limited: not all samples contribute equally, yet acquiring
arXiv:2606.23676v1 Announce Type: new Abstract: AdamW is the de facto optimizer for training large language models (LLMs), yet the theory behind it still lives mostly in finite-variance regimes. This
arXiv:2606.22174v1 Announce Type: new Abstract: Whole-body humanoid loco-manipulation requires coordinating the robot's entire kinematic chain. However, most existing systems typically decouple the up
arXiv:2606.20754v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance in robotic manipulation, but reliable uncertainty quantification remains challenging,
arXiv:2606.22890v1 Announce Type: new Abstract: Optical microscopy enables rapid, label-free imaging of live bacteria and is the standard instrument for species identification across clinical, environ
arXiv:2606.23604v1 Announce Type: new Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation. How
arXiv:2510.04646v2 Announce Type: replace Abstract: Flow matching models generate high-fidelity molecular geometries but incur significant computational costs during inference, requiring hundreds of n
arXiv:2606.22660v1 Announce Type: new Abstract: Several disruptive research directions have recently emerged in computer vision, including foundation models achieving previously unseen zero-shot perfo
arXiv:2606.23593v1 Announce Type: cross Abstract: Robot-assisted minimally invasive surgery improves surgical precision but introduces complexity, making technical error detection essential for ensuri
arXiv:2606.21913v1 Announce Type: new Abstract: Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remar
arXiv:2606.21788v1 Announce Type: cross Abstract: Point-cloud goals provide a direct way to specify dexterous in-hand reorientation: instead of defining an object-specific pose frame or estimating 6D
arXiv:2606.17702v2 Announce Type: replace Abstract: Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extracti
arXiv:2606.23357v1 Announce Type: new Abstract: Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show tha
arXiv:2601.22679v2 Announce Type: replace-cross Abstract: Consistency models have been proposed for fast generative modeling, achieving results competitive with diffusion and flow models. However, the
arXiv:2606.22606v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deploymen
arXiv:2606.22724v1 Announce Type: new Abstract: Federated low-rank adaptation methods are attractive for fine-tuning large models under communication and privacy constraints, but heterogeneous client
arXiv:2606.22998v1 Announce Type: new Abstract: Text-conditioned motion generation is a promising interface for programming humanoid robots, yet current generators are often trained on human motion da
arXiv:2606.22589v1 Announce Type: new Abstract: Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific ex
arXiv:2606.22527v1 Announce Type: new Abstract: Diffusion and flow-based generative models produce strong images, yet their controllability remains largely endpoint-centric: users specify conditions a
arXiv:2606.23286v1 Announce Type: new Abstract: Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, in
arXiv:2606.22976v1 Announce Type: new Abstract: In this paper, we propose using random walks on graphs as a verifiable sandbox to study different parallel sampling strategies in masked diffusion model
arXiv:2606.21661v1 Announce Type: new Abstract: Generating a coherent multi-shot video requires structured cross-shot memory. Subject appearance, scene context, and speaker identity must persist acros
arXiv:2606.21434v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs for end-to-end representation learning. While RDL is evol
arXiv:2606.23243v1 Announce Type: new Abstract: Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We pro
arXiv:2606.21309v1 Announce Type: new Abstract: We introduce WildBox, a dataset and benchmark for monocular 3D detection of wildlife from drone video, comprising 237,505 3D bounding box annotations ac
arXiv:2606.22371v1 Announce Type: cross Abstract: Recent generative video compression methods leverage powerful generative priors to achieve perceptually pleasing reconstructions. However, most existi
arXiv:2606.11783v1 Announce Type: new Abstract: Recent progress in video generation has shown impressive visual synthesis capabilities. However, open-domain customized video generation remains limited
arXiv:2606.11751v1 Announce Type: cross Abstract: Multi-turn image editing is essential for iterative design, yet current models often struggle with identity drift and error accumulation over successi
arXiv:2606.11779v1 Announce Type: new Abstract: The need for detecting and sorting batteries is drastically increasing for many applications. This study proves the potential of transfer learning in pr
arXiv:2601.00181v3 Announce Type: replace-cross Abstract: We address two persistent gaps in Emotion Recognition in Conversation: which modeling choices materially affect performance, and how recogniti
arXiv:2606.12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures o
arXiv:2606.11925v1 Announce Type: new Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and ena
arXiv:2606.11408v1 Announce Type: new Abstract: Action chunking has become a standard design in modern robot policies, from diffusion/flow policies to vision-language-action models, where the policy p
arXiv:2606.11710v1 Announce Type: new Abstract: This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such
arXiv:2606.11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approxi
arXiv:2606.11828v1 Announce Type: cross Abstract: Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy d
arXiv:2606.12303v1 Announce Type: new Abstract: Multimodal image fusion aims to integrate complementary information from different modalities into a fused image that preserves rich local details while
arXiv:2606.11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are li
arXiv:2606.11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds. However, the computational bu
arXiv:2606.12195v1 Announce Type: new Abstract: Recent progress in foundation models has shifted toward agentic behavior involving multi-step reasoning and tool use. However, open-source efforts large
arXiv:2606.12042v1 Announce Type: new Abstract: Deep Reinforcement Learning (DRL) has shown promise for social navigation, yet its real-world deployment remains hindered by a persistent sim-to-real ga
arXiv:2409.12707v2 Announce Type: replace-cross Abstract: Fluidic injection offers a promising solution to improve the performance of the overexpanded single expansion ramp nozzles (SERNs) during vehi
arXiv:2606.11431v1 Announce Type: new Abstract: Mirror Descent (MD) extends Gradient Descent (GD) beyond Euclidean geometry and has recently reappeared as a lens for KL-regularized policy optimization
arXiv:2602.00424v2 Announce Type: replace Abstract: Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but in
arXiv:2606.12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing
arXiv:2606.11243v1 Announce Type: cross Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology. While diffusion-based and fl
arXiv:2606.12397v1 Announce Type: cross Abstract: Router is the cornerstone component to the Mixture-of-Experts models. Serving as expert proxies, the rows of the router matrix compute their similarit
arXiv:2606.11846v1 Announce Type: new Abstract: Current virtual staining approaches offer the potential for time- and cost-efficient biomarker quantification in cancer diagnostics and prognostics. How
arXiv:2606.12213v1 Announce Type: new Abstract: Panoramic imagery is increasingly used in world-generation, games, and simulation, where users may need not only photorealistic scenes but also stylized
arXiv:2606.11304v1 Announce Type: cross Abstract: We introduce SPADE (SPlit And Delay Embeddings), an autoregressive transformer for sequences whose tokens carry multiple features. Rather than embeddi
arXiv:2606.11489v1 Announce Type: new Abstract: Real-world robots need to adapt their behavior beyond the envelope of their pre-trained policy. Policy finetuning or retraining are options, but they ri
arXiv:2606.11990v1 Announce Type: cross Abstract: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive featur
arXiv:2606.11625v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) are increasingly explored as predictive experts within emerging agentic time-series systems. However, TSFMs exhibi
arXiv:2606.11314v1 Announce Type: new Abstract: We introduce TRON, a rendering framework that combines 3D Gaussian ray tracing with neural rendering to enable realistic and controllable rendering of r