Proposes a deep learning-based real-time 3D human model with dynamic shape, motion, and textures learned from multi-view videos without detailed 3D supervision.

Marc Habermann, Lingjie Liu, Weipeng Xu et al.

Model-based driving policy using offline logs and Bellman equation evaluation, achieving 25% higher score with 40× less data.

Dian Chen, Vladlen Koltun, Philipp Krähenbühl

Scaling XLM-R to 3.5B and 10.7B parameters significantly improves cross-lingual understanding, surpassing previous models on benchmarks.

Naman Goyal, Jingfei Du, Myle Ott et al.

GODIVA combines VQ-VAE and 3D sparse attention to generate open-domain videos; RM reaches 93.48 on MSR-VTT.

Chenfei Wu, Lun Huang, Qianxi Zhang et al.

Proposes a layered neural representation (ST-NeRF) for editable, high-quality free-viewpoint video of large dynamic scenes using sparse 16 cameras.

Jiakai Zhang, Xinhang Liu, Xinyi Ye et al.

Introduced P3M-10k dataset and P3M-Net for privacy-preserving portrait matting, achieving state-of-the-art results with face-blurred images.

Jizhizi Li, Sihan Ma, Jing Zhang et al.

ViLD employs vision-language knowledge distillation to enable open-vocabulary object detection, achieving 16.1 mask AP_r on LVIS.

Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo et al.

Proposes 8 sanity checks for reward functions, revealing widespread flaws in autonomous driving RL reward design.

W. Bradley Knox, Alessandro Allievi, Holger Banzhaf et al.

Proposes a method using dense correlation volumes to estimate extreme rotation in RGB image pairs, successfully handling non-overlapping images.

Ruojin Cai, Bharath Hariharan, Noah Snavely et al.

Introduces FRANK benchmark with fine-grained factuality error classification, evaluating summarization models and metrics.

Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov

MDETR performs text-conditioned end-to-end detection, pre-trained on 1.3M aligned image-text pairs for open-vocabulary grounding.

Aishwarya Kamath, Mannat Singh, Yann LeCun et al.

Introduces InfographicVQA dataset and evaluates Transformer-based models, revealing significant performance gaps in understanding complex infographics.

Minesh Mathew, Viraj Bagal, Rubèn Pérez Tito et al.

Proposes importance sampling to accelerate PINNs training, improving convergence by ~30% and reducing training time by 20%.

Mohammad Amin Nabian, Rini Jasmine Gladstone, Hadi Meidani

DeepImpact leverages semantic impact scores with BERT and DocT5Query to improve first-stage retrieval by 17%, enabling faster and more accurate search.

Antonio Mallia, Omar Khattab, Nicola Tonellotto et al.

Proposes a deep video matting framework with spatio-temporal feature aggregation, outperforming traditional and deep image methods.

Yanan Sun, Guanzhi Wang, Qiao Gu et al.

Reset-Free Reinforcement Learning via Multi-Task Learning achieves complex dexterous manipulation without human intervention.

Abhishek Gupta, Justin Yu, Tony Z. Zhao et al.

Constructed H2O dataset and proposed a graph convolutional network for joint 3D hand-object interaction recognition from RGB images.

Taein Kwon, Bugra Tekin, Jan Stuhmer et al.

Proposes a transferable deep learning framework combining GFNet and Mosaic predictor for unseen PDE domains, achieving 3 orders speedup.

Hengjie Wang, Robert Planas, Aparna Chandramowlishwaran et al.

Proposes dataset inference leveraging model memorization to verify ownership with over 99% confidence, using statistical tests and distance estimation techniques.

Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

FIERY employs end-to-end deep learning to predict multi-modal future trajectories in bird’s-eye view from monocular cameras, outperforming baselines with IoU of 57.8%.

Anthony Hu, Zak Murez, Nikhil Mohan et al.