Posters

Multi-view Masked Contrastive Representation Learning (M²CRL) significantly boosts endoscopic video analysis by using a novel multi-view masking strategy and contrastive learning, achieving state-of-t…

Multi-turn RLHF surpasses single-turn methods by aligning LLMs with human preferences across entire conversations, not just individual turns. A novel mirror-descent algorithm, MTPO, is introduced, pr…

Ref-MC2 reconstructs high-fidelity 3D objects with inter-reflections by using a novel multi-times Monte Carlo sampling strategy, achieving superior performance in accuracy and efficiency.

Multi-Stage Predict+Optimize tackles optimization problems where parameters are revealed sequentially, improving predictions and decisions through stage-wise updates.

MSVMamba: A novel multi-scale vision model leveraging state-space models, achieves high accuracy in image classification and object detection while maintaining linear complexity, solving the long-rang…

S3F: a novel multi-scale model achieves state-of-the-art protein fitness prediction by integrating protein sequence, structure, and surface features.

Hierarchical Sinkhorn Tree (HST) robustly retrieves accurate 3D point cloud correspondences using multi-scale consistency, outperforming state-of-the-art methods.

This paper introduces efficient algorithms, MR-NaS and DBMR-BPI, for identifying optimal policies across multiple reward functions in reinforcement learning, achieving competitive performance with the…

LVLMs often hallucinate objects, a problem worsened when multiple objects are present. This paper introduces ROPE, a novel automated evaluation protocol that reveals how object class distribution and…

D-LISA: Dynamic modules & language-informed spatial attention revolutionizes multi-object 3D grounding, surpassing state-of-the-art accuracy by 12.8%.

Adaptive multi-model ensemble conformal prediction achieves strongly adaptive regret, yielding more efficient prediction sets in dynamic environments.

IsoFormer, a novel multi-modal model, accurately predicts RNA transcript isoform expression by integrating DNA, RNA, and protein sequence information, achieving state-of-the-art results.

Boosting LLM collaboration, this research introduces a novel theoretical framework for multi-LLM debate, revealing key principles like the effect of similar models and interventions to enhance accurac…

Researchers created MMA, a large multilingual dataset of informal-formal mathematical pairs, leveraging a language model for reverse translation. Fine-tuned models achieved significantly improved aut…

SLAN: A novel approach for multi-label open-set recognition, enriching sub-labeling info using structural data to identify unknown labels.

Novel surrogate losses with label-independent H-consistency bounds enable stronger guarantees for multi-label learning.

MIPLMA, a novel algorithm, enhances multi-instance partial-label learning by dynamically adjusting margins for attention scores and predicted probabilities, leading to superior performance.

MHCDIFF: a novel pipeline using multi-hypotheses conditioned point cloud diffusion for accurate 3D human reconstruction from occluded images, outperforming state-of-the-art methods.

Multi-Head Mixture-of-Experts (MH-MoE) drastically boosts large language model efficiency by activating almost all expert networks, achieving superior performance compared to existing Sparse Mixture-o…

Multi-group Proportional Representation (MPR) tackles skewed search results by measuring representation across intersectional groups, improving fairness in image retrieval.