Posts
These are casual study notes I jot down while reading papers.
Efficiently Reconstructing Dynamic Scenes OneĀ šÆ D4RTĀ at a Time
GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction
SAM 3D: 3Dfy Anything in Images
World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning
When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models
MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Vision-Flan: Scaling Human-Labeled Tasks in Visual Instruction Tuning
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Flamingo: a Visual Language Model for Few-Shot Learning
[MiniGPT-4] Enhancing Vision-Language Understanding with Advanced Large Language Models
Improved Baselines with Visual Instruction Tuning (LLaVA-1.5)
Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
Lecture 14: Reasoning
Lecture 11: High-Resolution, High-Performing LVLMs
Lecture 10: Large Vision Language Models (LVLMs)
Uni3D: Exploring Unified 3D Representation at Scale
ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding
Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material
Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models
LLaVA:Ā LargeĀ LanguageĀ andĀ VisionĀ Assistant
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning