Search: “models”

arXiv:2606.01149v1 Announce Type: new Abstract: Video Moment Retrieval (MR) and Highlight Detection (HD) are crucial tasks in video analysis that aim to localize specific moments and estimate clip-wis

arXiv:2512.23351v2 Announce Type: replace Abstract: The flexibility and accuracy of methods for automatically counting objects in images and videos are limited by the way the object can be specified.

arXiv:2606.01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms. While aerial rob

arXiv:2606.01014v1 Announce Type: cross Abstract: We address text-based 3D human motion editing, where the goal is to preserve the style and structure of a source motion while applying edits described

arXiv:2606.02303v1 Announce Type: new Abstract: Detecting dead trees in aerial imagery is vital for assessing forest health, especially as tree mortality increases globally due to climate change, but

arXiv:2307.06647v4 Announce Type: replace-cross Abstract: We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific co

arXiv:2606.01628v1 Announce Type: cross Abstract: Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence

arXiv:2606.01361v1 Announce Type: new Abstract: People often see animal shapes in clouds, a phenomenon known as pareidolia. We propose an AI-based method that aims to predict which animals people are

arXiv:2402.14031v2 Announce Type: replace-cross Abstract: This paper presents an autoencoder with ordered variance (AEO), in which the conventional reconstruction loss is augmented by a variance-based

arXiv:2606.02002v1 Announce Type: new Abstract: Blind image quality assessment (BIQA) aims to predict perceived image quality without access to a reference image. Classical natural scene statistics (N

arXiv:2606.00313v1 Announce Type: cross Abstract: Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-wo

arXiv:2511.04791v2 Announce Type: replace Abstract: Modern LLM serving systems must sustain high throughput while meeting strict latency SLOs across two distinct inference phases: compute-intensive pr

arXiv:2606.01590v1 Announce Type: new Abstract: Modern vehicle platforms are equipped with a rich sensor suite, including LiDAR, calibrated multi-camera rigs, and accurate ego-motion, that in principl

arXiv:2502.16174v4 Announce Type: replace-cross Abstract: Although modern LLMs are aligned with human values during post-training, robust moderation remains essential to prevent harmful outputs at dep

arXiv:2606.01601v1 Announce Type: new Abstract: Visual explainability for object detection remains challenging due to the multi-instance nature of detection. Existing approaches predominantly adopt po

arXiv:2509.23544v2 Announce Type: replace-cross Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric posit

arXiv:2503.06473v5 Announce Type: replace-cross Abstract: Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly

arXiv:2606.01271v1 Announce Type: new Abstract: The rapid growth of the satellite industry has driven a significant increase in geospatial data acquisition, highlighting a critical bottleneck: the sev

arXiv:2606.01573v1 Announce Type: new Abstract: Gaussian splatting has shown strong potential for 3D reconstruction and novel view synthesis. However, most existing methods require accurate camera par

arXiv:2511.15645v3 Announce Type: replace Abstract: Pedestrian inertial odometry (PIO) estimates autonomous pedestrian motion using only acceleration and angular velocity measurements collected by an

arXiv:2606.01954v1 Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space m

arXiv:2505.18492v5 Announce Type: replace Abstract: Mathematical competition problems fall into two broad types: theorem proving, which asks for a proof of a given statement, and answer construction,

arXiv:2606.00803v1 Announce Type: cross Abstract: Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in c

arXiv:2511.06663v2 Announce Type: replace-cross Abstract: Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challe

arXiv:2606.00432v1 Announce Type: new Abstract: As retrieval-augmented generation (RAG) systems scale, it becomes increasingly challenging to ensure faithful grounding in external evidence. Large lang

arXiv:2606.00349v1 Announce Type: cross Abstract: Reconstructing spatiotemporal fields from partial observations is fundamental to scientific inference, from inferring atmospheric states from satellit

arXiv:2502.15411v4 Announce Type: replace-cross Abstract: Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Busines

arXiv:2506.16704v3 Announce Type: replace Abstract: We study a fundamental question of domain generalization: given a family of domains (i.e., data distributions), how many randomly sampled domains do

arXiv:2606.00371v1 Announce Type: new Abstract: Muon optimizers improve neural-network training by replacing ill-conditioned momentum updates with approximately semi-orthogonal updates. This motivates

arXiv:2601.22328v2 Announce Type: replace Abstract: Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, n

arXiv:2606.00260v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living. In realistic deploy

arXiv:2602.20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.

arXiv:2606.00605v1 Announce Type: new Abstract: Transformers have achieved remarkable success across a wide range of applications, and a growing body of work suggests that part of their strength comes

arXiv:2312.03644v3 Announce Type: replace Abstract: Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learn

arXiv:2606.00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performanc

arXiv:2606.00666v1 Announce Type: cross Abstract: Transition metal complexes are central to catalysis, drug design, and materials science, with relevant properties strongly sensitive to their three-di

arXiv:2606.00675v1 Announce Type: new Abstract: Water research in Brazil largely overlooks the widespread damming of small streams for agricultural uses such as watering cattle, farm-scale hydropower,

arXiv:2606.02494v1 Announce Type: cross Abstract: Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the

arXiv:2606.01538v1 Announce Type: cross Abstract: To study the ability to infer physical dynamics from videos and extrapolate them forward in time, we assemble a dataset of 2D Material Point Method (M

arXiv:2606.02147v1 Announce Type: cross Abstract: Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring c

arXiv:2606.02432v1 Announce Type: new Abstract: Task-oriented dexterous grasp generation aims to produce dexterous grasp poses that are both physically plausible and functionally suitable for specifie

arXiv:2606.02223v1 Announce Type: new Abstract: Estimating the generative mechanism of large-scale networks is a fundamental challenge in statistical machine learning. It requires the identification o

arXiv:2602.01753v3 Announce Type: replace Abstract: Aligning objects with corresponding textual descriptions is a fundamental challenge and a realistic requirement in vision-language understanding. Wh

arXiv:2606.00990v1 Announce Type: new Abstract: A mobile robot following a graph of known routes can make costly navigation errors when a temporary obstacle blocks a critical edge: waiting too long be

arXiv:2404.01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness

arXiv:2606.00056v1 Announce Type: cross Abstract: This study develops a dimensionless multi-domain physics-informed neural network (PINN) framework for electro-osmotic radial consolidation considering