Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks187 from public data
- DehazeNet: An End-to-End System for Single Image Haze Removal3,391
This paper proposes a trainable end-to-end system called DehazeNet, for medium transmission estimation, which takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model.
- Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation843
Key to Fantasia3D is the disentangled modeling and learning of geometry and appearance, which is more compatible with popular graphics engines, supporting relighting, editing, and physical simulation of the generated 3D assets.
- Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks553
Factorized spatio-temporal convolutional networks (FstCN) are proposed that factorize the original 3D convolution kernel learning as a sequential process of learning 2D spatial kernels in the lower layers, followed by learning 1D temporal kernel in the upper layers.
- Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection511
A novel method termed Frustum ConvNet (F-ConvNet), which aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN).
- Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal511
A novel method termed Frustum ConvNet (F-ConvNet), which aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN).
- Domain-Symmetric Networks for Adversarial Domain Adaptation356
This paper proposes a new domain adaptation method called Domain-Symmetric Networks (SymNets), which is based on a symmetric design of source and target task classifiers, based on which an additional classifier is constructed that shares with them its layer neurons.
- Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering331
This work describes the proposed method as Structurally Regularized Deep Clustering (SRDC), where it enhances target discrimination with clustering of intermediate network features, and enhance structural regularization with soft selection of less divergent source examples.
- 308
- GS-IR: 3D Gaussian Splatting for Inverse Rendering300
The GS-IR is a novel inverse rendering approach based on 3D Gaussian Splatting that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results and demonstrates the superiority of the method over baseline methods through qualitative and quantitative evaluations of various challenging scenes.
- 293
- Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization269
This work proposes to perform Exact Feature Distribution Matching (EFDM) by exactly matching the empirical Cumulative Distribution Functions (eCDFs) of image features, which could be implemented by applying the Exact Histogram Matching(EHM) in the image feature space.
- Discriminative Adversarial Domain Adaptation234
Discriminative Adversarial Domain Adaptation has a novel adversarial objective that encourages a mutually inhibitory relation between category and domain predictions for any input instance and it defines a minimax game that can promote the joint distribution alignment.
- Deep Mesh Reconstruction From Single RGB Images via Topology Modification Networks225
This paper presents an end-to-end single-view mesh reconstruction framework that is able to generate high-quality meshes with complex topologies from a single genus-0 template mesh and outperforms the current state-of-the-art methods both qualitatively and quantitatively.
- 215
- 215
- Lattice Long Short-Term Memory for Human Action Recognition209
This work proposes Lattice-LSTM (L2STM), which extends LSTM by learning independent hidden state transitions of memory cells for individual spatial locations, which effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity.
- Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots207
Inspired by compositional models, an object is represented as composition of its interior non-empty voxels, termed hotspots, and the spatial relations of hotspots gives rise to the representation of Object as Hotspots (OHS).
- SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation204
SAM-6D, a novel framework designed to realize the task of zero-shot 6D object pose estimation through two steps, including instance segmentation and pose estimation of novel objects, outperforms the existing methods on the seven core datasets of the BOP Benchmark.
- 3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding194
Comprehensive results on the contributed dataset show the promise of visual affordance understanding as a valuable yet challenging benchmark.
- DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency190
A new method of Dual Pose Network with refined learning of pose consistency for category-level 6D object pose and size estimation, shortened as DualPoseNet, which outperforms existing methods with a large margin in the regime of high precision.
- Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing188
An in-depth probing analysis is conducted and it is demonstrated that cross-attention maps in Stable Diffusion often contain object attribution information, which can result in editing failures, and proposes a simplified, yet more stable and efficient, tuning-free procedure that modifies only the self-attention maps of specified attention layers during the denoising process.
- 184
- Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks177
An end-to-end solution of Semantic Superpoint Tree Network (SSTNet) for proposing object instances from scene points, and design in SSTNet a refinement module, termed CliqueNet, to prune superpoints that may be wrongly grouped into instance proposals.
- Orthogonal Deep Neural Networks169
This paper proves that DNNs are of local isometry on data distributions of practical interest, and establishes a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks’ weight matrices.
- Surface Reconstruction From Point Clouds: A Survey and a Benchmark166
The present paper contributes a large-scale benchmarking dataset consisting of both synthetic and real-scanned data that takes into account various sensing imperfections that are commonly encountered in practical depth scanning, and conducts thorough empirical studies by comparing existing methods on the constructed benchmark.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.