6 citations · 16 across the 20 of their papers we have counts for
19 papers · 1 filter
TerraScope: Pixel-Grounded Visual Reasoning for Earth Observation
Yan Shu, Bin Ren, Zhitong Xiong +4
Vision-language models (VLMs) have shown promise in earth observation (EO), yet they struggle with tasks that require grounding complex spatial reasoning in precise pixel-level vis…
Panoramic Affordance Prediction
Zixin Zhang, Chenfei Liao, Hongfei Zhang +10
Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from n…
DVD: Deterministic Video Depth Estimation with Generative Priors
Hongfei Zhang, Harold Haodong Chen, Chenfei Liao +12
Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discriminative models demand…
Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off
Davide Lobba, Fulvio Sanguigni, Bin Ren +3
Recent advances in Virtual Try-On (VTON) and Virtual Try-Off (VTOFF) have greatly improved photo-realistic fashion synthesis and garment reconstruction. However, existing datasets…
Loomis Painter: Learning Painting Processes by Reversing Image-to-Video Generation
Markus Pobitzer, Chang Liu, Chenyi Zhuang +3
Step-by-step painting tutorials are used for learning artistic techniques, but existing video resources lack interactivity and personalization. While recent generative models have…
SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting
Mengjiao Ma, Qi Ma, Yue Li +10
3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven…
Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation
Bin Ren, Eduard Zamfir, Zongwei Wu +7
Restoring multiple degradations efficiently via just one model has become increasingly significant and impactful, especially with the proliferation of mobile devices. Traditional s…
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
Xu Zheng, Ziqiao Weng, Yuanhuiyi Lyu +7
Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs)…
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
Yawei Li, Bin Ren, Jingyun Liang +5
While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degr…
SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining
Yue Li, Qi Ma, Runyi Yang +10
Recognizing arbitrary or previously unseen categories is essential for comprehensive real-world 3D scene understanding. Currently, all existing methods rely on 2D or textual modali…
CityLoc: 6DoF Pose Distributional Localization for Text Descriptions in Large-Scale Scenes with Gaussian Representation
Qi Ma, Runyi Yang, Bin Ren +4
Localizing textual descriptions within large-scale 3D scenes presents inherent ambiguities, such as identifying all traffic lights in a city. Addressing this, we introduce a method…
Hierarchical Information Flow for Generalized Efficient Image Restoration
Yawei Li, Bin Ren, Jingyun Liang +5
While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To…
ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining
Qi Ma, Yue Li, Bin Ren +5
3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To…
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning
Bin Ren, Guofeng Mei, Danda Pani Paudel +6
Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cl…
Restore Anything Model via Efficient Degradation Adaptation
Bin Ren, Eduard Zamfir, Zongwei Wu +6
With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful. Traditional approaches ty…
Sharing Key Semantics in Transformer Makes Efficient Image Restoration
Bin Ren, Yawei Li, Jingyun Liang +6
Image Restoration (IR), a classic low-level vision task, has witnessed significant advancements through deep models that effectively model global information. Notably, the emergenc…
Key-Graph Transformer for Image Restoration
Bin Ren, Yawei Li, Jingyun Liang +5
While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, espe…
Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation
Ti Wang, Mengyuan Liu, Hong Liu +5
Although data-driven methods have achieved success in 3D human pose estimation, they often suffer from domain gaps and exhibit limited generalization. In contrast, optimization-bas…
ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
Weijie Wang, Wenqi Ren, Guofeng Mei +5
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders g…