Search Results for author:
Found 59 papers, 31 papers with code
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution.
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3).
HumanTOMATO: Text-aligned Whole-body Motion Generation
This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent facial expressions, hand gestures, and body motions simultaneously.
UniPose: Detecting Any Keypoints
This work proposes a unified framework called UniPose to detect keypoints of any articulated (e. g., human and animal), rigid, and soft objects via visual or textual prompts for fine-grained vision understanding and manipulation.
Ranked #1 on
2D Human Pose Estimation
on Human-Art
(using extra training data)
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection
In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boosts detection performance.
Molecular Conformation Generation via Shifting Scores
Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule.
FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions
To facilitate the development of 3D pose estimation, we present FreeMan, the first large-scale, multi-view dataset collected under the real-world conditions.
Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models
In this paper, we introduce a novel multi-dancer synthesis task called partner dancer generation, which involves synthesizing virtual human dancers capable of performing dance with users.
Neural Interactive Keypoint Detection
Click-Pose explores how user feedback can cooperate with a neural keypoint detector to correct the predicted keypoints in an interactive way for a faster and more effective annotation process.
Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset
In this paper, we present Motion-X, a large-scale 3D expressive whole-body motion dataset.
YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp Detection
YONA fully exploits the information of one previous adjacent frame and conducts polyp detection on the current frame without multi-frame collaborations.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model
Extensive experiments demonstrate that DiffHOI significantly outperforms the state-of-the-art in regular detection (i. e., 41. 50 mAP) and zero-shot detection.
Ranked #2 on
Zero-Shot Human-Object Interaction Detection
on HICO-DET
(using extra training data)
Human-Object Interaction Detection
Zero-Shot Human-Object Interaction Detection
Hierarchical Weight Averaging for Deep Neural Networks
Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs).
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i. e. various levels of label granularity) to train a powerful human parsing network.
Inherent Consistent Learning for Accurate Semi-supervised Medical Image Segmentation
Semi-supervised medical image segmentation has attracted much attention in recent years because of the high cost of medical image annotations.
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.
Ranked #2 on
2D Human Pose Estimation
on Human-Art
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification
We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages.
Representation Learning
Video-Based Person Re-Identification
Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis
Specifically, this paper introduces a simple but effective point cloud cross-modality training (PointCMT) strategy, which utilizes view-images, i. e., rendered or projected 2D images of the 3D object, to boost point cloud analysis.
Ranked #10 on
3D Point Cloud Classification
on ModelNet40
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications.
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion.
Ranked #3 on
3D Semantic Segmentation
on SemanticKITTI
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency
By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities.
Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency
Integrating multi-modal data to promote medical image analysis has recently gained great attention.
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
A simple pixel selection strategy followed with the construction of multi-level contrastive units is introduced to optimize the model for both domain adaptation and active supervised learning.
MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning
Dancing video retargeting aims to synthesize a video that transfers the dance movements from a source video to a target person.
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples
This work proposes a novel framework named MetaCloth via meta-learning, which is able to learn unseen tasks of dense fashion landmark detection with only a few annotated samples.
End-to-End Dense Video Captioning with Parallel Decoding
Dense video captioning aims to generate multiple associated captions with their temporal locations from the video.
Ranked #6 on
Dense Video Captioning
on ActivityNet Captions
Shallow Attention Network for Polyp Segmentation
To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation.
Ranked #9 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Crowd Counting via Perspective-Guided Fractional-Dilation Convolution
One of the main issues in this task is how to handle the dramatic scale variations of pedestrians caused by the perspective effect.
Multi-Compound Transformer for Accurate Biomedical Image Segmentation
The recent vision transformer(i. e. for image classification) learns non-local attentive interaction of different patch tokens.
PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond
Extensive experiments demonstrate the effectiveness of both PolarMask and PolarMask++, which achieve competitive results on instance segmentation in the challenging COCO dataset with single-model and single-scale training and testing, as well as new state-of-the-art results on rotate text detection and cell segmentation.
Ranked #78 on
Instance Segmentation
on COCO test-dev
(using extra training data)
PointLIE: Locally Invertible Embedding for Point Cloud Sampling and Recovery
In this paper, we address a fundamental problem in PCSR: How to downsample the dense point cloud with arbitrary scales while preserving the local topology of discarding points in a case-agnostic manner (i. e. without additional storage for point relationship)?
Parser-Free Virtual Try-on via Distilling Appearance Flows
A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.
Ranked #1 on
Virtual Try-on
on MPV
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging.
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input.
Ranked #3 on
3D Semantic Scene Completion
on SemanticKITTI
3D Semantic Scene Completion from a single RGB image
3D Semantic Segmentation
+3
Polygon-free: Unconstrained Scene Text Detection with Box Annotations
For example, without using polygon annotations, PSENet achieves an 80. 5% F-score on TotalText [3] (vs. 80. 9% of fully supervised counterpart), 31. 1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network.
Exemplar Normalization for Learning Deep Representation
This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content
First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
Ranked #4 on
Virtual Try-on
on VITON
(IS metric)
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once
Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods.
Switchable Normalization for Learning-to-Normalize Deep Representation
Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer?
SSN: Learning Sparse Switchable Normalization via SparsestMax
Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax.
DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images
A strong baseline is proposed, called Match R-CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner.
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Our results suggest that (1) using distinct normalizers improves both learning and generalization of a ConvNet; (2) the choices of normalizers are more related to depth and batch size, but less relevant to parameter initialization, learning rate decay, and solver; (3) different tasks and datasets have different behaviors when learning to select normalizers.
Learning Deep Representations for Semantic Image Parsing: a Comprehensive Overview
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field of computer vision.
Attentive Crowd Flow Machines
Traffic flow prediction is crucial for urban traffic management and public safety.
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN).
Differentiable Learning-to-Normalize via Switchable Normalization
We hope SN will help ease the usage and understand the normalization techniques in deep learning.
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Rather than relying on elaborative annotations (e. g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images.
Progressively Diffused Networks for Semantic Image Segmentation
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application.
Cost-Effective Active Learning for Deep Image Classification
In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.
Deep Structured Scene Parsing by Learning with Image Descriptions
This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i. e., a semantic object hierarchy with object interaction relations) that finely accords with human perception.
Geometric Scene Parsing with Hierarchical LSTM
This paper addresses the problem of geometric scene parsing, i. e. simultaneously labeling geometric surfaces (e. g. sky, ground and vertical plane) and determining the interaction relations (e. g. layering, supporting, siding and affinity) between main regions.
Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification
Furthermore, each bit of our hashing codes is unequally weighted so that we can manipulate the code lengths by truncating the insignificant bits.
Deep Boosting: Layered Feature Mining for General Image Classification
Constructing effective representations is a critical but challenging problem in multimedia understanding.
Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling
During the iterations of inference, the model of each category is analytically updated by a generative learning algorithm.
