Knowledge catalogue
arXiv:2510.11068v4 Announce Type: replace Abstract: Real-world deployment often exposes vision models to distribution shifts, making test-time adaptation (TTA) important for reliable prediction. Exist
arXiv:2610.17552v1 Announce Type: new Abstract: Recent video super-resolution (VSR) models achieve efficient one-step generation through diffusion distillation. However, high-resolution inference rema
arXiv:2610.16914v1 Announce Type: new Abstract: Intraoperative frozen-section diagnosis of central nervous system (CNS) lesions guides surgical decisions but is challenged by artefacts, diagnostic com
arXiv:2610.18118v1 Announce Type: new Abstract: Visually similar actions can produce contact loss, jamming, slippage, or irreversible damage, while tactile feedback may arrive after a critical transit
arXiv:2610.14445v1 Announce Type: new Abstract: In film production, chroma keying composites an approved foreground performance into a new environment, but conventional workflows require coordinated b
arXiv:2610.17191v1 Announce Type: new Abstract: Linear-time sequence models typically store long-context information in a recurrent state. A fixed-size state limits how much information can be stored,
arXiv:2610.17859v1 Announce Type: new Abstract: Thermal-to-visible (T2V) face translation has important applications in nighttime biometric recognition, low-light surveillance, and human interpretatio
arXiv:2610.16236v1 Announce Type: new Abstract: Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations
arXiv:2610.16364v1 Announce Type: new Abstract: Speech-driven visual grounding in remote sensing (RSVG) removes the typing bottleneck of text-based interaction and is intended to preserve prosodic int
arXiv:2610.16558v1 Announce Type: cross Abstract: World models capture environment dynamics and support counterfactual reasoning for planning. In image-goal navigation, where the target often lies bey
arXiv:2610.15280v1 Announce Type: new Abstract: Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objec
arXiv:2610.16654v1 Announce Type: new Abstract: Flow matching models are typically trained to transport a Gaussian source to the data distribution, with source and target samples paired independently.
arXiv:2610.15401v1 Announce Type: new Abstract: VLMs provide promising solutions to embodied reward design. Most current methods that train VLMs as reward regressors not only require extensive annotat
arXiv:2610.17684v1 Announce Type: new Abstract: High-fidelity surgical video generation is increasingly important for surgical education, simulation, and downstream AI training, as conventional simula
arXiv:2610.15715v1 Announce Type: cross Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support on
arXiv:2610.16047v1 Announce Type: cross Abstract: Automatic extraction and anatomical numbering (R01-R12, L01-L12) of all 24 rib centerlines in thoracic CT is essential for unfolded rib visualization,
arXiv:2610.17061v1 Announce Type: cross Abstract: We study in-context binary classification in a simple attention model trained to minimise a logistic loss. Each prompt has a latent logistic teacher,
arXiv:2610.16652v1 Announce Type: new Abstract: We introduce IND-Eval, a public benchmark that organizes industrial time series across domains into source-specific forecasting tasks. Relative to estab
arXiv:2610.16784v1 Announce Type: new Abstract: Generating and editing presentation slides requires models to combine accurate text rendering, instruction following, visual grounding, aesthetic design
arXiv:2610.18855v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction.
arXiv:2511.14110v2 Announce Type: replace-cross Abstract: Neonatal seizures carry substantial risks of acute brain injury and long-term neurodevelopmental impairment, motivating the need for timely id
arXiv:2610.15613v1 Announce Type: cross Abstract: Effective unsupervised domain adaptation (UDA) requires a representation that captures transferable structure from both the labeled source domain and
arXiv:2610.18072v1 Announce Type: new Abstract: We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. L
arXiv:2610.14726v1 Announce Type: new Abstract: Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use
arXiv:2610.15379v1 Announce Type: cross Abstract: Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose
arXiv:2610.15840v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning
arXiv:2610.18604v1 Announce Type: new Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because
arXiv:2610.17877v1 Announce Type: new Abstract: Reusing Transformer blocks offers a way to increase diffusion-model computation at test time, but naive recurrence can degrade generation beyond its tra
arXiv:2610.18873v1 Announce Type: cross Abstract: Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, subop
arXiv:2511.17366v2 Announce Type: replace-cross Abstract: Building a generalist robot that can perceive, reason, and act across diverse tasks remains an open challenge, especially for dexterous manipu
arXiv:2610.17792v1 Announce Type: new Abstract: GUI grounding enables web agents to translate natural-language instructions into actionable interface locations. However, dense layouts and small, visua
arXiv:2610.16482v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models have made remarkable progress in generating high-fidelity visuals, yet their outputs still often exhibit physically
arXiv:2610.16906v1 Announce Type: new Abstract: Multi-modal fusion for 3D object detection assumes that cameras and LiDAR are broadly complementary. In practice, how much each modality is trusted at a
arXiv:2610.17001v1 Announce Type: new Abstract: Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution tim
arXiv:2610.18407v1 Announce Type: new Abstract: Diffusion language models reconstruct masked tokens in parallel, offering an alternative to autoregressive generation. Yet their Transformer backbones t
arXiv:2610.16586v1 Announce Type: new Abstract: Generating accurate 3D molecules requires jointly determining a variable number of atoms, their identities, and geometry across spatial scales: global s
arXiv:2509.07552v3 Announce Type: replace Abstract: We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previo
arXiv:2610.17591v1 Announce Type: new Abstract: Estimating simulation-ready material parameters from visual observations is crucial for deformable-object manipulation but challenging because material
arXiv:2610.14770v1 Announce Type: new Abstract: Multimodal contrastive learning typically relies on manually specified scoring functions, such as cosine similarity, to quantify the similarity between
arXiv:2610.18054v1 Announce Type: cross Abstract: Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks
arXiv:2610.16851v1 Announce Type: new Abstract: Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches ofte
arXiv:2610.16302v1 Announce Type: new Abstract: Video-based interactive world models aim to enable real-time, user-controllable environments. Yet most existing systems are trained and rolled out at a
arXiv:2610.14998v1 Announce Type: new Abstract: Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the numb
arXiv:2610.16082v1 Announce Type: new Abstract: Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions
arXiv:2610.16763v1 Announce Type: new Abstract: Relational learning typically predicts from a database state, but decision making asks a different question: how will that state change under an action?
arXiv:2610.14583v1 Announce Type: cross Abstract: Autoregressive robot video world models based on discrete visual tokens remain unreliable over long rollout horizons. Retaining the entire generated h
arXiv:2610.14921v1 Announce Type: new Abstract: A language model that keeps learning online must not slowly erode its own base capabilities. We ask which part of a modern LLM should absorb continual u
arXiv:2610.18039v1 Announce Type: new Abstract: Vision Transformers achieve strong performance but incur high computational costs from quadratic token interactions. Training-free token reduction metho
arXiv:2610.15734v1 Announce Type: new Abstract: We present ScenePlacer, a framework for scene-level pose estimation of objects generated by state-of-the-art native shape generation models, placing gen
arXiv:1307.2965v3 Announce Type: replace Abstract: The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilag
arXiv:2610.14082v1 Announce Type: cross Abstract: Robotic perception must remain reliable when extreme environmental conditions severely degrade the primary RGB observation. Although robotic platforms
arXiv:2610.16297v1 Announce Type: new Abstract: Scaling laws predict large-model performance from small experiments, but finding a reliable law itself requires costly experiments. SLDBench tests wheth
arXiv:2610.15660v1 Announce Type: new Abstract: Domain generalised semantic segmentation (DGSS) has recently benefited from combining multiple foundation models, such as vision-language and vision fou
arXiv:2610.17323v1 Announce Type: new Abstract: Face recognition systems are deployed widely enough that a photograph shared online can be matched without its subject's consent. Generator-based protec
arXiv:2610.15560v1 Announce Type: new Abstract: Existing navigation systems commonly rely on dedicated depth sensors or explicit depth reconstruction, while learning to generate trajectories directly
arXiv:2610.16338v1 Announce Type: new Abstract: Recent years have witnessed rapid progress in joint audio--visual generation. However, audio--visual synchronization remains a challenge, as generated s
arXiv:2610.14513v1 Announce Type: new Abstract: Robotic platforms combine multi-modal sensors to operate across diverse conditions, but most dense correspondence methods are limited to RGB-RGB or a sm
arXiv:2606.16767v2 Announce Type: replace Abstract: Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are se
arXiv:2610.18655v1 Announce Type: new Abstract: Texture interpolation asks what should lie between two textures. Standard methods typically blend pixels or features at corresponding spatial locations,
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and finetuning a base unimodal model for bi-modal generation. Diverging f