8464 papers
The paper introduces FlyPose, a lightweight, real-time human pose estimation pipeline optimized for aerial UAV views that achieves significant accuracy improvements across multiple datasets and is successfully deployed on-board a quadrotor, accompanied by the release of a new challenging dataset called FlyPose-104.
This paper presents InsSo3D, a robust SLAM framework that fuses 3D sonar point clouds with Inertial Navigation System data to enable accurate, large-scale 3D mapping and drift correction for underwater inspections in turbid environments.
The paper introduces OnlineSI, a framework that enables Multimodal Large Language Models to continuously improve spatial understanding and grounding in dynamic environments by maintaining a finite spatial memory and integrating 3D point cloud data with semantic information.
This paper introduces SRA 2, a lightweight intrinsic guidance framework that accelerates diffusion transformer training and improves generation quality by aligning intermediate latent features with pre-trained VAE features via a simple projection layer, eliminating the need for external encoders or dual-model setups while incurring minimal computational overhead.
This paper addresses the inadequacy of traditional safety-case frameworks for modern AI systems by introducing comprehensive taxonomies of claims, arguments, and evidence, along with a reusable, structured template designed to construct credible and adaptive safety cases for generative and agentic AI.
The paper introduces FARTrack, a fast autoregressive visual tracking framework that leverages Task-Specific Self-Distillation and Inter-frame Autoregressive Sparsification to achieve real-time, high-performance tracking (70.6% AO on GOT-10k) with exceptional inference speeds of up to 343 FPS on GPU and 121 FPS on CPU.
The paper introduces SpatialReward, a reward model that leverages explicit spatial reasoning to overcome the "Attention Collapse" limitation in existing evaluators, thereby providing fine-grained, accurate signals that significantly enhance online reinforcement learning performance for image editing tasks.
The paper proposes (MGS)-Net, a geometry-grounded framework that unifies Micro-Geometric Scale Adaptation and Macro-Geometric Structure Filtering to overcome geometric misalignment and achieve state-of-the-art cross-view geo-localization performance.
The paper presents APEX, a deep reinforcement learning framework that enables a 29-DoF Unitree G1 humanoid robot to autonomously traverse platforms up to 114% of its leg length by composing perceptive climbing, walking, and reconfiguration skills through a novel ratchet progress reward and robust sim-to-real perception strategies.
This paper proposes RL-Co, a reinforcement learning-based sim-real co-training framework that combines supervised fine-tuning on mixed real and simulated data with interactive simulation fine-tuning anchored by real-world data, achieving significant improvements in real-world success rates, generalization, and data efficiency for Vision-Language-Action models.
DAV-GSWT is a data-efficient framework that combines diffusion priors with active view sampling to synthesize high-fidelity Gaussian Splatting Wang Tiles from minimal input observations, enabling the generation of expansive, photorealistic landscapes without relying on densely sampled exemplar reconstructions.
This paper proposes "Operational Agency," a legal framework utilizing an "Operational Agency Graph" to trace and apportion human culpability in autonomous AI systems by evaluating their goal-directedness, foresight, and safety architecture, thereby ensuring accountability without granting AI legal personhood.
The paper proposes RobSelf, a robust self-supervised model that jointly optimizes a misalignment-aware feature translator and a content-aware reference filter to achieve state-of-the-art cross-modal super-resolution on real-world misaligned data with significantly improved efficiency.
UrbanAlign proposes a novel post-hoc calibration framework that aligns frozen vision-language models with human preferences for urban scene assessment by mining interpretable dimensions, extracting robust concept scores via an Observer-Debater-Judge chain, and calibrating them through locally-weighted ridge regression, achieving state-of-the-art accuracy without any model retraining.
This paper demonstrates that affordance reasoning in Vision Foundation Models can be achieved in a zero-shot, training-free manner by fusing DINO's inherent geometric part structures with Flux's verb-conditioned interaction priors, thereby establishing geometric and interaction perception as the fundamental, composable building blocks of affordance understanding.
StoryTailor is a zero-shot pipeline that generates temporally coherent, action-rich multi-subject visual narratives on a single RTX 4090 by synergizing Gaussian-Centered Attention, Action-Boost Singular Value Reweighting, and a Selective Forgetting Cache to simultaneously ensure action faithfulness, subject identity fidelity, and cross-frame background continuity.
The paper introduces UniVBench, a comprehensive benchmark featuring 200 high-quality, human-created multi-shot videos and a unified agentic evaluation system (UniV-Eval) to holistically assess video foundation models across understanding, generation, editing, and reconstruction tasks, addressing the limitations of existing fragmented and task-specific evaluations.
This paper introduces a geometry-aware Graph Neural Network autodecoder that leverages protein-structure priors and ellipsoidal support lifting to achieve higher accuracy in heterogeneous single-particle cryo-EM reconstruction compared to traditional MLP-based methods.
The paper introduces DPCache, a training-free acceleration framework for diffusion models that formulates sampling as a global path planning problem and utilizes dynamic programming on a path-aware cost tensor to select optimal key timesteps, thereby achieving significant speedups with minimal quality loss and even surpassing full-step baselines in certain metrics.
This paper proposes a unified, query-dependent learning-to-rank model that effectively merges heterogeneous retrieval channels for large-scale e-commerce search by jointly optimizing business KPIs and capturing short-term user intent, resulting in a 2.85% conversion lift and deployment on Target.com while meeting strict latency constraints.