Collections
Discover the best community collections!
Collections including paper arxiv:2504.16072
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 86 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
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Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 66 -
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
Paper • 2410.09604 • Published -
Geospatial Mechanistic Interpretability of Large Language Models
Paper • 2505.03368 • Published • 13 -
Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation
Paper • 2505.02836 • Published • 8
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Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization
Paper • 2504.08641 • Published • 6 -
PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home Clusters
Paper • 2504.08791 • Published • 140 -
Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 66 -
A Survey of Interactive Generative Video
Paper • 2504.21853 • Published • 46
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 31 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
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CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
Paper • 2404.15653 • Published • 29 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 14 -
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Paper • 2405.12130 • Published • 50 -
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
Paper • 2405.12981 • Published • 33
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TTRL: Test-Time Reinforcement Learning
Paper • 2504.16084 • Published • 122 -
Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 66 -
Reinforcing General Reasoning without Verifiers
Paper • 2505.21493 • Published • 27 -
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
Paper • 2505.24760 • Published • 74
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R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
Paper • 2503.10615 • Published • 17 -
UniGoal: Towards Universal Zero-shot Goal-oriented Navigation
Paper • 2503.10630 • Published • 6 -
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Paper • 2503.09516 • Published • 40 -
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
Paper • 2503.07536 • Published • 88
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 31 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 45 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 24
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 86 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
Paper • 2404.15653 • Published • 29 -
MoDE: CLIP Data Experts via Clustering
Paper • 2404.16030 • Published • 14 -
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Paper • 2405.12130 • Published • 50 -
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
Paper • 2405.12981 • Published • 33
-
Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 66 -
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
Paper • 2410.09604 • Published -
Geospatial Mechanistic Interpretability of Large Language Models
Paper • 2505.03368 • Published • 13 -
Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation
Paper • 2505.02836 • Published • 8
-
TTRL: Test-Time Reinforcement Learning
Paper • 2504.16084 • Published • 122 -
Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 66 -
Reinforcing General Reasoning without Verifiers
Paper • 2505.21493 • Published • 27 -
REASONING GYM: Reasoning Environments for Reinforcement Learning with Verifiable Rewards
Paper • 2505.24760 • Published • 74
-
Training-free Guidance in Text-to-Video Generation via Multimodal Planning and Structured Noise Initialization
Paper • 2504.08641 • Published • 6 -
PRIMA.CPP: Speeding Up 70B-Scale LLM Inference on Low-Resource Everyday Home Clusters
Paper • 2504.08791 • Published • 140 -
Describe Anything: Detailed Localized Image and Video Captioning
Paper • 2504.16072 • Published • 66 -
A Survey of Interactive Generative Video
Paper • 2504.21853 • Published • 46
-
R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
Paper • 2503.10615 • Published • 17 -
UniGoal: Towards Universal Zero-shot Goal-oriented Navigation
Paper • 2503.10630 • Published • 6 -
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Paper • 2503.09516 • Published • 40 -
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
Paper • 2503.07536 • Published • 88