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Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
arXiv:2603.12046v2 Announce Type: replace-cross Abstract: Audio-Visual Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains unclear. We present Dr. SHAP-AV, a framework using Shapley values to analyze modality contributions in AVSR.
How Sean Baker went from high school AV guy to Oscar-winning director
After scooping the Oscars for Anora, Sean Baker brings his filmmaking tips to Sydney's Vivid Festival Thu 4 Jun 2026 at 4:30am Growing up in New Jersey before the internet and multiplexes branched into art-house films, Anora filmmaker Sean Baker got his first taste of cinema like any American kid living in the suburbs. "It was Spielberg and Lucas — mainstream Hollywood fair," Baker tells ABC Arts. Mainlining magazines like Premier, Famous Monsters and Fangoria in his bedroom, Baker regularly...
Do Joint Audio-Video Generation Models Understand Physics?
arXiv:2605.07061v2 Announce Type: replace Abstract: Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visual physics, or merely generate plausible sounds and frames that violate real-world consistency? We introduce AV-Phys Bench, a benchmark for evaluating physical commonsense in joint audio-video generation. AV-Phys Bench tests models across three scene categories: Steady State, Event Transition,...
Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
Announce Type: replace Abstract: Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking. Reinforcement learning (RL) has emerged as a powerful method for controlling AVs, but scaling to fleets (essential for multi-target tracking or rapidly moving targets) is challenging. Multi-Agent RL (MARL) is notoriously sample-inefficient, and while high-fidelity simulators like Gazebo's LRAUV provide up to 100x faster-than-real-time single-robot...
Re-imagining ISO 26262 in the Age of Autonomous Vehicles: Enhancing Controllability through Transferability and Predictability
Announce Type: new Abstract: The ISO 26262 standard defines functional safety for road vehicles through risk assessments based on Severity, Exposure, and Controllability, grounded in a human-driven vehicle paradigm. In the context of autonomous vehicles (AVs), the absence of a human driver necessitates revisiting these principles. This paper decomposes the Controllability placeholder into two auditable evidence dimensions of ISO 26262 by introducing two measurable sub-concepts:...
CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles
Announce Type: new Abstract: Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer. This design overlooks the emerging paradigm of cooperative autonomy, where vehicles interact with roadside units (RSUs), edge servers, and cloud-hosted intelligence through vehicle-to-everything (V2X) connectivity. Cooperative...
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
Announce Type: new Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic. Although >30m is often labeled long-range in computer vision, on roadways it affords only approx.
Ten years after India, US Marine Corps bids farewell to Sea Harrier
The US Marine Corps is retiring its AV-8B Sea Harrier Jump Jet, a decade after India's navy did. This unique aircraft's Vertical Take-Off and Landing capability allowed operations from dispersed locations and smaller carriers, proving vital for close air support and deployments. This technology lives on in the F-35B.
Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning
arXiv:2606.08136v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used for autonomous-vehicle (AV) motion planning, but its real-time applicability is often limited by the need for accurate models and online solution of nonlinear, nonconvex optimization problems in dynamic road environments. Actor-critic reinforcement learning offers a promising alternative for online policy generation, yet its policy-learning process often lacks explicit control-theoretic structure....
Stanley Cup odds: Carolina odds-on favorite over V...
The Hurricanes remain the Stanley Cup favorites, with the Golden Knights just behind on the odds board after sweeping the Avs.