Shap·E employs conditional diffusion to directly generate 3D implicit function parameters, enabling rapid, diverse 3D asset creation.

Heewoo Jun, Alex Nichol

Prompt Diffusion enables in-context learning in diffusion models, trained on six vision-language tasks for strong generalization.

Zhendong Wang, Yifan Jiang, Yadong Lu et al.

Introduces DataComp benchmark, utilizing 128B image-text pairs with filtering strategies, achieving 79.2% ImageNet zero-shot accuracy with ViT-L/14, outperforming prior datasets.

Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang et al.

AMT achieves efficient video frame interpolation using bidirectional pixel correlations and multi-field transforms, enhancing PSNR.

Zhen Li, Zuo-Liang Zhu, Ling-Hao Han et al.

NeuralField-LDM employs hierarchical latent diffusion models to generate complex, realistic 3D scenes, outperforming state-of-the-art methods with significant improvements in FID and FVD scores.

Seung Wook Kim, Bradley Brown, Kangxue Yin et al.

LLaVA: leveraging GPT-4 generated multimodal instruction data, achieves 85.1% relative score, with 92.53% accuracy on Science QA, pioneering multimodal instruction tuning.

Haotian Liu, Chunyuan Li, Qingyang Wu et al.

Proposes a multi-view transformer-based method with epipolar sampling for high-quality novel view synthesis from a single wide-baseline stereo pair, outperforming prior sparse observation approaches.

Yilun Du, Cameron Smith, Ayush Tewari et al.

Proposed certified black-box defense using Robust UNet denoiser with 35% improvement on CIFAR-10.

Astha Verma, A V Subramanyam, Siddhesh Bangar et al.

InterGen employs diffusion models with cooperative transformers and a large-scale multimodal dataset to generate diverse two-person interaction motions guided by text.

Han Liang, Wenqian Zhang, Wenxuan Li et al.

Single-gate MoE achieves comparable efficiency and accuracy to complex models, outperforming non-mixture baselines.

Amelie Royer, Ilia Karmanov, Andrii Skliar et al.

StillFast introduces an end-to-end model for short-term object interaction anticipation, achieving 13.29% Top-5 mAP on EGO4D v2, surpassing SOTA.

Francesco Ragusa, Giovanni Maria Farinella, Antonino Furnari

SAM model supports prompt-based image segmentation with over 1 billion masks, achieving state-of-the-art zero-shot performance.

Alexander Kirillov, Eric Mintun, Nikhila Ravi et al.

GINA-3D generates tri-plane implicit assets from Waymo sensor data, achieving FID 59.5 on WOD-Vehicle.

Bokui Shen, Xinchen Yan, Charles R. Qi et al.

EGC integrates energy-based and diffusion models, achieving state-of-the-art performance in both image classification (78.9% accuracy on CIFAR-10) and high-fidelity image generation (FID 6.05 on ImageNet-1k).

Qiushan Guo, Chuofan Ma, Yi Jiang et al.

IPMAN integrates biomechanical stability via pressure heatmaps, CoP, and CoM, improving 3D human pose accuracy and physical plausibility by 15% on standard datasets.

Shashank Tripathi, Lea Müller, Chun-Hao P. Huang et al.

This paper introduces ToMe for Stable Diffusion, reducing tokens by 60%, doubling generation speed, and saving 5.6× memory without retraining.

Daniel Bolya, Judy Hoffman

Proposes NeRF-supervised deep stereo training, generating synthetic data from single-camera images, achieving 30-40% performance gains without ground-truth.

Fabio Tosi, Alessio Tonioni, Daniele De Gregorio et al.

LLaMA-Adapter uses zero-initialized attention with only 1.2M parameters to achieve instruction tuning comparable to full fine-tuning.

Renrui Zhang, Jiaming Han, Chris Liu et al.

CARTO achieves category- and joint-agnostic 3D reconstruction from a single stereo image, improving mAP 3D IOU50 by 20.4%.

Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov et al.

Proposes Anti-DreamBooth using adversarial noise to disrupt personalized diffusion model training, reducing fake image generation by over 85%.

Thanh Van Le, Hao Phung, Thuan Hoang Nguyen et al.