Search Results for author:
Found 34 papers, 11 papers with code
Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach
At inference, we generate images with arbitrary expansion multiples by inputting an anchor image and its corresponding positional embeddings.
Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models
This paper presents Paint3D, a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs.
ChartLlama: A Multimodal LLM for Chart Understanding and Generation
Next, we introduce ChartLlama, a multi-modal large language model that we've trained using our created dataset.
InfMLLM: A Unified Framework for Visual-Language Tasks
Our experiments demonstrate that preserving the positional information of visual embeddings through the pool-adapter is particularly beneficial for tasks like visual grounding.
Ranked #62 on
Visual Question Answering
on MM-Vet
Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering
In this paper, we propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations.
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data
However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models.
Ranked #53 on
Visual Question Answering
on MM-Vet
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation
Second, we propose an align-guided contrastive loss to refine the alignment of vision and text embeddings.
ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo
to provide additional consistency constraints, which grows GPU memory consumption and complicates the model's structure and training pipeline.
Improved Neural Radiance Fields Using Pseudo-depth and Fusion
To make the constructed volumes as close as possible to the surfaces of objects in the scene and the rendered depth more accurate, we propose to perform depth prediction and radiance field reconstruction simultaneously.
Points-to-3D: Bridging the Gap between Sparse Points and Shape-Controllable Text-to-3D Generation
To better utilize the sparse 3D points, we propose an efficient point cloud guidance loss to adaptively drive the NeRF's geometry to align with the shape of the sparse 3D points.
SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting
Recurrent predictions for future atmospheric fields are firstly performed at 1. 40625-degree resolution, and then a diffusion-based super-resolution model is leveraged to recover the high spatial resolution and finer-scale atmospheric details.
UniNeXt: Exploring A Unified Architecture for Vision Recognition
Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone.
D2Q-DETR: Decoupling and Dynamic Queries for Oriented Object Detection with Transformers
In this paper, we propose an end-to-end framework for oriented object detection, which simplifies the model pipeline and obtains superior performance.
Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation
In this work, we propose a novel and data-efficient framework for WILSS, named FMWISS.
LMSeg: Language-guided Multi-dataset Segmentation
Instead of relabeling each dataset with the unified taxonomy, a category-guided decoding module is designed to dynamically guide predictions to each datasets taxonomy.
Efficient Mask Correction for Click-Based Interactive Image Segmentation
To this end, we propose an efficient method to correct the mask with a lightweight mask correction network.
PolyBuilding: Polygon Transformer for End-to-End Building Extraction
Given a set of polygon queries, the model learns the relations among them and encodes context information from the image to predict the final set of building polygons with fixed vertex numbers.
Over-the-Air Computation: Foundations, Technologies, and Applications
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation.
Hierarchical Normalization for Robust Monocular Depth Estimation
In this paper, we address monocular depth estimation with deep neural networks.
MimCo: Masked Image Modeling Pre-training with Contrastive Teacher
Specifically, MimCo takes a pre-trained contrastive learning model as the teacher model and is pre-trained with two types of learning targets: patch-level and image-level reconstruction losses.
FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation
Inspired by the recent progress on semantic directions on feature-space, we propose to include augmentations in feature space for efficient distillation.
Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
More specifically, we use the pseudo-labels generated by a mean teacher to supervise the student network to achieve a mutual knowledge distillation between the two branches.
Semantic Data Augmentation based Distance Metric Learning for Domain Generalization
Further, we provide an in-depth analysis of the mechanism and rational behind our approach, which gives us a better understanding of why leverage logits in lieu of features can help domain generalization.
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells.
Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations
Point annotations are considerably more time-efficient than bounding box annotations.
Point RCNN: An Angle-Free Framework for Rotated Object Detection
To tackle this problem, we propose a purely angle-free framework for rotated object detection, called Point RCNN, which mainly consists of PointRPN and PointReg.
SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation
We also compare four categories of perturbation methods for ensemble forecasting, i. e. fixed distribution perturbation, learned distribution perturbation, MC dropout, and multi model ensemble.
Poseur: Direct Human Pose Regression with Transformers
We propose a direct, regression-based approach to 2D human pose estimation from single images.
Ranked #2 on
Keypoint Detection
on MS COCO
Get Better 1 Pixel PCK: Ladder Scales Correspondence Flow Networks for Remote Sensing Image Matching in Higher Resolution
Percentage of image size is often used as the threshold of PCK.
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
Previous works [3, 27] fail to employ strong augmentation in pseudo label learning efficiently, as the large distribution change caused by strong augmentation harms the batch normalisation statistics.
TFPose: Direct Human Pose Estimation with Transformers
We propose a human pose estimation framework that solves the task in the regression-based fashion.
Ranked #26 on
Pose Estimation
on MPII Human Pose
(using extra training data)
Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework
To alleviate the confirmation bias problem and improve the quality of pseudo annotations, we further propose a co-rectify scheme based on Instant-Teaching, denoted as Instant-Teaching$^*$.
Ranked #12 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Object Detection Made Simpler by Eliminating Heuristic NMS
On the COCO dataset, our simple design achieves superior performance compared to both the FCOS baseline detector with NMS post-processing and the recent end-to-end NMS-free detectors.
Federated Learning via Intelligent Reflecting Surface
To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements.
