3447 papers
NaviGait is a hierarchical framework that combines trajectory optimization and deep reinforcement learning to synthesize robust, intuitive bipedal locomotion by selecting and minimally morphing gaits from an offline library, thereby simplifying reward design and accelerating training while maintaining high fidelity to reference motions.
This paper presents a real-time neural video compression framework that unifies intra and inter coding within a single adaptive model and employs a simultaneous two-frame compression design to effectively handle disocclusion, prevent error propagation, and achieve a 12.1% BD-rate reduction over DCVC-RT while maintaining real-time performance.
This paper addresses the Connected Unlabeled Multi-Agent Pathfinding (CUMAPF) problem by introducing PULL, a lightweight, complete algorithm that runs in time per step and outperforms both an Integer Linear Programming approach and naive methods in scalability and efficiency for swarm robotics applications.
This paper challenges the conventional assumption that reactive replanning requires updating existing plans by demonstrating that using fast almost-surely asymptotically optimal (ASAO) algorithms to solve a series of independent planning problems offers a more efficient and effective approach for navigating changing environments.
This paper demonstrates that selecting an optimal subset of body landmarks combined with spline-based imputation enables isolated Brazilian Sign Language (LIBRAS) recognition that is both 5 times faster and as accurate as state-of-the-art methods, overcoming the speed-accuracy trade-off of previous OpenPose-based approaches.
This paper introduces FRIDA, a lightweight and data-efficient framework that leverages features from pre-trained Stable Diffusion models to achieve state-of-the-art cross-generator fake image detection and source attribution through a training-free k-Nearest Neighbour approach and a compact neural classifier.
EgoMI is a framework that bridges the human-robot embodiment gap in imitation learning by capturing synchronized end-effector and active head trajectories from egocentric human demonstrations and utilizing a memory-augmented policy to enable robust whole-body manipulation on semi-humanoid robots.
The paper proposes SPAN, a novel framework for monocular 3D object detection that enhances geometric consistency and performance by introducing Spatial Point Alignment and 3D-2D Projection Alignment within a Hierarchical Task Learning strategy to overcome the limitations of traditional decoupled prediction paradigms.
This paper introduces V-Attack, a novel adversarial attack method for Large Vision-Language Models that achieves precise local semantic manipulation by targeting disentangled value features within transformer attention blocks, thereby overcoming the controllability limitations of existing approaches that rely on entangled patch-token representations.
The paper introduces AFRO, a self-supervised framework that learns dynamics-aware 3D visual representations by modeling state-action-state transitions via a generative diffusion process, thereby significantly improving robotic manipulation performance across diverse simulated and real-world tasks without requiring explicit action or reconstruction supervision.
This paper introduces the first formal framework for Audio-Visual World Models (AVWM), presenting the AVW-4k dataset and the AV-CDiT model to enable high-fidelity, synchronized simulation of binaural audio and visual dynamics that significantly enhances agent performance in continuous navigation tasks.
This paper demonstrates that a simple exact method can solve nearly all classical benchmark instances of the Traveling Salesman Problem with Time Windows in under ten seconds, revealing that these datasets are no longer representative for evaluating algorithmic performance or training machine learning models due to their exploitable structure.
This paper introduces AVGGT, a training-free acceleration framework that leverages an analysis of global attention's distinct roles in VGGT and to implement a two-step optimization strategy, achieving up to 10 inference speedup on long sequences while maintaining or improving accuracy in dense multi-view 3D reconstruction tasks.
UniBYD is a unified framework that leverages a unified morphological representation and a dynamic reinforcement learning algorithm with a hybrid shadow engine to bridge the embodiment gap, enabling robotic hands to transcend human imitation and discover manipulation policies optimally adapted to their own physical morphologies.
This paper introduces Directional Decoupling Alignment (D-Align), a novel framework that mitigates Preference Mode Collapse in diffusion reinforcement learning by applying directional corrections to reward signals, thereby preserving generative diversity while achieving superior human preference alignment.
This paper introduces Preguss, a modular framework that combines static analysis with LLM-aided synthesis to automatically generate and refine interprocedural specifications, enabling highly automated verification of large-scale programs (over 1,000 lines of code) while significantly reducing human effort.
To overcome the generalization failure of vanilla CLIP in face forgery detection caused by "low-rank spurious bias," this paper proposes SeLop, a causal representation learning method that identifies and removes spurious correlations via orthogonal low-rank subspace intervention, thereby achieving state-of-the-art performance with high robustness using only 0.39M trainable parameters.
This paper proposes a goal-centric framework for assessing Requirements Engineering methods for Privacy by Design, arguing that practitioners should evaluate these methods based on organizational goals rather than solely on process characteristics to better support their selection and tailoring.
This paper introduces CovertComBench, a specialized benchmark for evaluating Large Language Models in wireless covert communication, revealing that while current models excel at conceptual understanding and code generation, they significantly struggle with the rigorous mathematical derivations required for security-constrained optimization.
This paper proposes a novel AI-generated image detection method that exploits common final architectural components across diverse generators to "contaminate" real images for training, achieving 98.83% average accuracy on unseen generators by leveraging a taxonomy of 21 models and a DINOv3 backbone.