Ego3DLM jointly predicts past and future 3D body pose and motion descriptions in a single autoregressive pass, conditioned on egocentric video, 3D scene features, and three-point tracking, achieving state-of-the-art on the Nymeria benchmark.
In: International Conference on Learning Representations (2019)
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Ink3D decouples geometry from texture by generating dense orbit videos with a conditional video model and baking them via a neural optimizer to produce complex 3D textures.
MG-RWKV combines bidirectional RWKV, multi-granularity mixture of experts, and cross-granularity consistency to achieve state-of-the-art temporal forgery localization with linear complexity.
AVTok is a unified tokenizer that converts audio-video pairs into a compact 1D latent representation via dual-stream transformer and hierarchical training for improved reconstruction and cross-modal generation.
A noise-aware contrastive loss derived from temporal structure in colonoscopy videos produces polyp tracklet representations that outperform prior self-supervised and supervised baselines on retrieval, re-identification, size estimation, and histology tasks.
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
LLaBIT is a single instruction-finetuned LLM that performs report generation, VQA, segmentation, and translation on brain MRI images while outperforming task-specific models.
citing papers explorer
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Ego-Human Motion Prediction with 3D-Aware LLM
Ego3DLM jointly predicts past and future 3D body pose and motion descriptions in a single autoregressive pass, conditioned on egocentric video, 3D scene features, and three-point tracking, achieving state-of-the-art on the Nymeria benchmark.
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Ink3D: Sculpting 3D Assets with Extremely Complex Textures via Video Generative Models
Ink3D decouples geometry from texture by generating dense orbit videos with a conditional video model and baking them via a neural optimizer to produce complex 3D textures.
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MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization
MG-RWKV combines bidirectional RWKV, multi-granularity mixture of experts, and cross-granularity consistency to achieve state-of-the-art temporal forgery localization with linear complexity.
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AVTok: 1D Unified Tokenization for Holistic Audio-Video Generation
AVTok is a unified tokenizer that converts audio-video pairs into a compact 1D latent representation via dual-stream transformer and hierarchical training for improved reconstruction and cross-modal generation.
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Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
A noise-aware contrastive loss derived from temporal structure in colonoscopy videos produces polyp tracklet representations that outperform prior self-supervised and supervised baselines on retrieval, re-identification, size estimation, and histology tasks.
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LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
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Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Tasks
LLaBIT is a single instruction-finetuned LLM that performs report generation, VQA, segmentation, and translation on brain MRI images while outperforming task-specific models.
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