Posters

In multi-agent imitation learning, achieving regret equivalence is harder than value equivalence; this paper introduces novel algorithms that efficiently minimize the regret gap under various assumpti…

Madoc: A novel multi-agent framework calibrates RL policies for new environments using limited offline data, achieving superior performance in various locomotion tasks.

SeqComm, a novel multi-level communication scheme, tackles multi-agent coordination by leveraging asynchronous decision-making and a two-phase communication process for improved efficiency and theoret…

MTGS: a unified framework jointly predicts gaze and social gaze (shared attention, mutual gaze) for multiple people in videos, achieving state-of-the-art results using a temporal transformer model and…

MSPE empowers Vision Transformers to handle any image resolution by cleverly optimizing patch embedding, achieving superior performance on low-resolution images and comparable results on high-resoluti…

MSAGPT: Revolutionizing protein structure prediction by generating accurate virtual MSAs from limited data, boosting prediction accuracy by up to +8.5% TM-Score!

Self-supervised generative model MSA-Generator boosts protein structure prediction accuracy by producing high-quality MSAs, especially for challenging sequences lacking homologs.

MR-Ben: A new benchmark reveals LLMs’ meta-reasoning flaws, pushing the boundaries of AI evaluation beyond simple accuracy.

MoVA, a novel MLLM, enhances multimodal understanding by adaptively routing and fusing task-specific vision experts for improved generalization across diverse image content.

MotionTTT: Deep learning enables accurate 3D motion-corrected MRI by cleverly estimating motion parameters during test-time training, significantly improving image reconstruction.

MotionGS enhances deformable 3D Gaussian splatting for dynamic scenes by using motion flow to guide deformation, significantly improving reconstruction accuracy and outperforming state-of-the-art meth…

MotionCraft: Physics-based zero-shot video generation creates realistic videos with complex motion dynamics by cleverly warping the noise latent space of an image diffusion model using optical flow fr…

Motion Graph unleashes efficient and accurate video prediction by transforming video frames into interconnected graph nodes, capturing complex motion patterns with minimal computational cost.

Boosting video diffusion: Motion Consistency Model (MCM) disentangles motion and appearance learning for high-fidelity, fast video generation using few sampling steps.

Motif-oriented influence maximization tackles viral marketing’s challenge of reaching groups by proving a greedy algorithm with guaranteed approximation ratio and near-linear time complexity.

MoTE: A novel framework harmonizes generalization and specialization for visual-language video knowledge transfer, achieving state-of-the-art results.

MOTE-NAS: A new multi-objective training-based estimate drastically improves neural architecture search efficiency, achieving state-of-the-art accuracy with significantly reduced costs.

Adaptive greedy algorithms significantly improve the accuracy of identifying the most influential subset of training data, overcoming limitations of existing methods that fail to capture complex inter…

Monomial-NFNs boost neural network efficiency by leveraging scaling/sign-flipping symmetries, resulting in fewer trainable parameters and competitive performance.

MonoMAE enhances monocular 3D object detection by using depth-aware masked autoencoders to effectively handle object occlusions, achieving superior performance on both occluded and non-occluded object…