Proposes a cross-view dense video captioning framework using adversarial learning to transfer knowledge from web instructional videos to egocentric videos.

Takehiko Ohkawa, Takuma Yagi, Taichi Nishimura et al.

Introduces Molecular Conformer Fields (MCF), a scalable diffusion model directly predicting 3D atomic positions, surpassing previous structure assumptions.

Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly et al.

Proposes CCoT, a zero-shot chain-of-thought method using scene graphs to improve large multimodal models' compositional reasoning, boosting benchmark scores.

Chancharik Mitra, Brandon Huang, Trevor Darrell et al.

MagicAnimate uses diffusion models with temporal attention and appearance encoders to produce long, coherent human animations, outperforming baselines by over 38%.

Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew et al.

SeeSR leverages degradation-aware semantic prompts to guide diffusion models, achieving realistic super-resolution with strong semantic fidelity.

Rongyuan Wu, Tao Yang, Lingchen Sun et al.

Introduced MMMU, a benchmark with 11.5K multimodal questions across 6 disciplines, challenging models in expert-level perception and reasoning.

Xiang Yue, Yuansheng Ni, Kai Zhang et al.

Mip-Splatting introduces 3D frequency-constrained smoothing and Mip filtering to eliminate aliasing artifacts in Gaussian Splatting, improving multi-scale view synthesis.

Zehao Yu, Anpei Chen, Binbin Huang et al.

MeshGPT uses decoder-only Transformer with quantized geometric vocabulary to generate compact, sharp 3D meshes, improving shape coverage by 9%.

Yawar Siddiqui, Antonio Alliegro, Alexey Artemov et al.

First survey of legal LLMs: Transformer/ChatGPT promise efficiency, but data, ethics, and due process remain bottlenecks.

Jinqi Lai, Wensheng Gan, Jiayang Wu et al.

Proposes Stable Video Diffusion with a three-stage training strategy, leveraging large-scale curated datasets to significantly improve high-resolution video generation.

Andreas Blattmann, Tim Dockhorn, Sumith Kulal et al.

Automated lane merging using game theory and branch model predictive control, validated in dense traffic scenarios.

Luyao Zhang, Shaohang Han, Sergio Grammatico

AutoEval-Video is a benchmark for evaluating large vision-language models in video QA; GPT-4V excels but lags behind human accuracy of 72.8%.

Xiuyuan Chen, Yuan Lin, Yuchen Zhang et al.

This study systematically evaluates GPT-4V's capabilities in geographic and geospatial tasks via a custom benchmark, revealing strengths in fine-grained recognition and spatial reasoning.

Jonathan Roberts, Timo Lüddecke, Rehan Sheikh et al.

Proposes NIR as a unified instruction set for neuromorphic systems, enabling cross-platform model reproduction across 7 simulators and 4 hardware platforms.

Jens E. Pedersen, Steven Abreu, Matthias Jobst et al.

ZeroPS transfers 2D pretrained models SAM and GLIP to zero-shot 3D part segmentation without fine-tuning, based on multi-view relations.

Yuheng Xue, Nenglun Chen, Jun Liu et al.

Proposes leverage score-based sampling for RKHS numerical integration, reducing computational cost while maintaining optimal error rates.

Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito et al.

D3PO method directly fine-tunes diffusion models using human preferences without reward models, achieving performance comparable to reward-based methods.

Kai Yang, Jian Tao, Jiafei Lyu et al.

Introduces ShareGPT4V dataset with 1.2M detailed image captions, significantly boosting large multimodal model performance, achieving multiple SOTA results.

Lin Chen, Jinsong Li, Xiaoyi Dong et al.

Proposes a dual-scale ordinal shading framework for high-res intrinsic image decomposition, outperforming SOTA with detailed, globally consistent results.

Chris Careaga, Yağız Aksoy

SuGaR employs surface-aligned Gaussian splatting for fast, high-quality mesh extraction, enabling real-time scene editing and rendering.

Antoine Guédon, Vincent Lepetit