Search Results for author:
Found 17 papers, 11 papers with code
Transforming Weather Data from Pixel to Latent Space
The downstream task further demonstrates that task models can apply to multiple PVS with low data costs in latent space and achieve superior performance compared to models in pixel space.
StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos
This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience.
DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets.
VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting
By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods, providing clear and accurate forecasting results of future vegetation states.
CV-VAE: A Compatible Video VAE for Latent Generative Video Models
Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization.
SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing
In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form language.
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
RS-Mamba for Large Remote Sensing Image Dense Prediction
RSM is specifically designed to capture the global context of remote sensing images with linear complexity, facilitating the effective processing of large VHR images.
Ranked #1 on
Road Segmentation
on Massachusetts Roads Dataset
Building change detection for remote sensing images
Change Detection
+3
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition
1) We propose four architectural guidelines for designing large-kernel ConvNets the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep.
VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
In combination with the existing text tokenizer and detokenizer, this framework allows for the encoding of interleaved image-text data into a multimodal sequence, which can subsequently be fed into the transformer model.
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
1) We propose four architectural guidelines for designing large-kernel ConvNets, the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep.
Ranked #1 on
Object Detection
on COCO 2017
(mAP metric)
Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization
We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS).
Ranked #2 on
Change Detection
on WHU Building Dataset
Making LLaMA SEE and Draw with SEED Tokenizer
We identify two crucial design principles: (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a 1D causal dependency, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs.
Sticker820K: Empowering Interactive Retrieval with Stickers
Therefore, we propose StickerCLIP as a benchmark model on the Sticker820K dataset.
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools.
Fisher Information Guidance for Learned Time-of-Flight Imaging
Indirect Time-of-Flight (ToF) imaging is widely applied in practice for its superiorities on cost and spatial resolution.
Distribution-Aware Adaptive Multi-Bit Quantization
In this paper, we explore the compression of deep neural networks by quantizing the weights and activations into multi-bit binary networks (MBNs).
