Search Results for author:
Found 94 papers, 42 papers with code
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting
User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e. g., green tea), identify the online users to whom the ad package should be targeted.
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
(1) Mutually-Refined Proposal Extraction.
GaussianObject: Just Taking Four Images to Get A High-Quality 3D Object with Gaussian Splatting
Then we construct a Gaussian repair model based on diffusion models to supplement the omitted object information, where Gaussians are further refined.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query
In DeLR, the query is based on region-level, and we only annotate the object region that is queried; 2) Instead of directly providing both localization and recognition annotations, we separately query the two components, and thus reduce the recognition budget with the pseudo class labels provided by the model.
Spectral Prompt Tuning:Unveiling Unseen Classes for Zero-Shot Semantic Segmentation
Recently, CLIP has found practical utility in the domain of pixel-level zero-shot segmentation tasks.
Cascade-Zero123: One Image to Highly Consistent 3D with Self-Prompted Nearby Views
We propose a cascade generation framework constructed with two Zero-1-to-3 models, named Cascade-Zero123, to tackle this issue, which progressively extracts 3D information from the source image.
Segment Any 3D Gaussians
Interactive 3D segmentation in radiance fields is an appealing task since its importance in 3D scene understanding and manipulation.
GaussianEditor: Editing 3D Gaussians Delicately with Text Instructions
Specifically, we first extract the region of interest (RoI) corresponding to the text instruction, aligning it to 3D Gaussians.
AiluRus: A Scalable ViT Framework for Dense Prediction
Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence.
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks.
4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
Representing and rendering dynamic scenes has been an important but challenging task.
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models
In recent times, the generation of 3D assets from text prompts has shown impressive results.
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Recently years have witnessed a rapid development of large language models (LLMs).
Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
Based on the STA score, we are able to progressively prune the tokens without introducing any additional parameters or requiring further re-training.
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners
In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model.
Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models
In this paper, we start with a conceptual definition of AGI and briefly review how NLP solves a wide range of tasks via a chat system.
ControlVideo: Training-free Controllable Text-to-Video Generation
Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling.
Segment Anything in 3D with Radiance Fields
The Segment Anything Model (SAM) emerges as a powerful vision foundation model to generate high-quality 2D segmentation results.
Attention Weighted Local Descriptors
Local features detection and description are widely used in many vision applications with high industrial and commercial demands.
Multi-modal Prompting for Low-Shot Temporal Action Localization
In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time.
SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations
Reverse engineering CAD models from raw geometry is a classic but strenuous research problem.
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios
In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown classes (i. e., not seen during training), they always predict them as one of the training classes.
Rethinking Visual Prompt Learning as Masked Visual Token Modeling
To explore prompt learning on the generative pre-trained visual model, as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification into the pre-trained masked visual token prediction.
Self Correspondence Distillation for End-to-End Weakly-Supervised Semantic Segmentation
In addition, to further improve the segmentation accuracy, we design a Variation-aware Refine Module to enhance the local consistency of pseudo-labels by computing pixel-level variation.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual Recognition
To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation.
Treating Pseudo-labels Generation as Image Matting for Weakly Supervised Semantic Segmentation
To solve this problem, we develop a Double Decoupled Class Activation Map (D2CAM) for Mat-Label to generate a high-quality trimap.
Feature Calibration Network for Occluded Pedestrian Detection
FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module.
Security Closure of IC Layouts Against Hardware Trojans
In this work, we proactively and systematically harden the physical layouts of ICs against post-design insertion of Trojans.
Motion-inductive Self-supervised Object Discovery in Videos
In this paper, we consider the task of unsupervised object discovery in videos.
Ranked #3 on
Unsupervised Object Segmentation
on DAVIS 2016
SdAE: Self-distillated Masked Autoencoder
We also analyze how to build good views for the teacher branch to produce latent representation from the perspective of information bottleneck.
Visual Recognition by Request
Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal.
Active Pointly-Supervised Instance Segmentation
In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object.
SARNet: Semantic Augmented Registration of Large-Scale Urban Point Clouds
Registering urban point clouds is a quite challenging task due to the large-scale, noise and data incompleteness of LiDAR scanning data.
Masked Autoencoders are Robust Data Augmentors
Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification.
Fast Dynamic Radiance Fields with Time-Aware Neural Voxels
A multi-distance interpolation method is proposed and applied on voxel features to model both small and large motions.
Gradient Concealment: Free Lunch for Defending Adversarial Attacks
Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks.
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
The past year has witnessed a rapid development of masked image modeling (MIM).
Deep Point Cloud Simplification for High-quality Surface Reconstruction
In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity.
MTLDesc: Looking Wider to Describe Better
Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context.
TAPE: Task-Agnostic Prior Embedding for Image Restoration
In this paper, we propose a novel approach that embeds a task-agnostic prior into a transformer.
The KFIoU Loss for Rotated Object Detection
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
One-Bit Active Query With Contrastive Pairs
The Yes query is treated as positive pairs of the queried category for contrastive pulling, while the No query is treated as hard negative pairs for contrastive repelling.
NeuSample: Neural Sample Field for Efficient View Synthesis
Neural radiance fields (NeRF) have shown great potentials in representing 3D scenes and synthesizing novel views, but the computational overhead of NeRF at the inference stage is still heavy.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #63 on
Semantic Segmentation
on Cityscapes test
Self-supervised Re-renderable Facial Albedo Reconstruction from Single Image
To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation which is reconstructed with the detailed albedo together.
Understanding Self-supervised Learning via Information Bottleneck Principle
To address this issue, we introduce the information bottleneck principle and propose the Self-supervised Variational Information Bottleneck (SVIB) learning framework.
Single-Image Specular Highlight Removal via Real-World Dataset Construction
Specular reflections pose great challenges on various multimedia and computer vision tasks, e. g. , image segmentation, detection and matching.
Bag of Instances Aggregation Boosts Self-supervised Distillation
Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag.
Multi-dataset Pretraining: A Unified Model for Semantic Segmentation
This is achieved by first pretraining the network via the proposed pixel-to-prototype contrastive loss over multiple datasets regardless of their taxonomy labels, and followed by fine-tuning the pretrained model over specific dataset as usual.
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
This paper introduces a novel multi-task model called Mixture of Virtual-Kernel Experts (MVKE) to learn user preferences on various actions and topics unitedly.
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens
Transformers have offered a new methodology of designing neural networks for visual recognition.
Semi-supervised Contrastive Learning with Similarity Co-calibration
Semi-supervised learning acts as an effective way to leverage massive unlabeled data.
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
Ranked #3 on
Medical Image Segmentation
on ACDC
Deep Deformation Detail Synthesis for Thin Shell Models
Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes.
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Ranked #16 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
In this paper, we propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to \textbf{Cross-samples and Multi-level} representation, and models the invariance to semantically similar images in a hierarchical way.
Scene text removal via cascaded text stroke detection and erasing
Specifically, we decouple the text removal problem into text stroke detection and stroke removal.
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?
Recently, contrastive learning has largely advanced the progress of unsupervised visual representation learning.
Center-wise Local Image Mixture For Contrastive Representation Learning
Contrastive learning based on instance discrimination trains model to discriminate different transformations of the anchor sample from other samples, which does not consider the semantic similarity among samples.
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
Neural architecture search (NAS) has attracted increasing attentions in both academia and industry.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results.
Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks
To learn the sampling policy, a Markov decision process is embedded into the search algorithm and a moving average is applied for better stability.
Distilling Object Detectors with Task Adaptive Regularization
Knowledge distillation, which aims at training a smaller student network by transferring knowledge from a larger teacher model, is one of the promising solutions for model miniaturization.
A survey on deep hashing for image retrieval
To this end, I propose a concept: shadow of the CNN output.
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i. e., shape factor, mean, and variance).
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples.
Ranked #22 on
Fine-Grained Image Classification
on CUB-200-2011
Circumventing Outliers of AutoAugment with Knowledge Distillation
AutoAugment has been a powerful algorithm that improves the accuracy of many vision tasks, yet it is sensitive to the operator space as well as hyper-parameters, and an improper setting may degenerate network optimization.
Ranked #185 on
Image Classification
on ImageNet
MGCN: Descriptor Learning using Multiscale GCNs
Second, we propose a new multiscale graph convolutional network (MGCN) to transform a non-learned feature to a more discriminative descriptor.
Latency-Aware Differentiable Neural Architecture Search
However, these methods suffer the difficulty in optimizing network, so that the searched network is often unfriendly to hardware.
Wasserstein-Bounded Generative Adversarial Networks
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
Capacity Preserving Mapping for High-dimensional Data Visualization
We provide a rigorous mathematical treatment to the crowding issue in data visualization when high dimensional data sets are projected down to low dimensions for visualization.
Central Similarity Quantization for Efficient Image and Video Retrieval
In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy.
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-network and searching for an optimal architecture.
Ranked #20 on
Neural Architecture Search
on CIFAR-10
Distilling Object Detectors with Fine-grained Feature Imitation
To address the challenge of distilling knowledge in detection model, we propose a fine-grained feature imitation method exploiting the cross-location discrepancy of feature response.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Few-shot Adaptive Faster R-CNN
To address these challenges, we first introduce a pairing mechanism over source and target features to alleviate the issue of insufficient target domain samples.
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization
First, we design and implement a base network, which can attain better performance in terms of classification accuracy and generalization (in most cases) compared with state-of-the-art methods.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Learning 3D Keypoint Descriptors for Non-Rigid Shape Matching
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.
ML-LocNet: Improving Object Localization with Multi-view Learning Network
This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision.
Zigzag Learning for Weakly Supervised Object Detection
Unlike them, we propose a zigzag learning strategy to simultaneously discover reliable object instances and prevent the model from overfitting initial seeds.
A Quantization-Friendly Separable Convolution for MobileNets
As deep learning (DL) is being rapidly pushed to edge computing, researchers invented various ways to make inference computation more efficient on mobile/IoT devices, such as network pruning, parameter compression, and etc.
Speeding Up the Bilateral Filter: A Joint Acceleration Way
To achieve the constant-time BF whose complexity is irrelevant to the kernel size, many techniques have been proposed, such as 2D box filtering, dimension promotion, and shiftability property.
Hardware-Efficient Guided Image Filtering For Multi-Label Problem
In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance.
Ensemble of Part Detectors for Simultaneous Classification and Localization
Part-based representation has been proven to be effective for a variety of visual applications.
Picking Deep Filter Responses for Fine-Grained Image Recognition
Recognizing fine-grained sub-categories such as birds and dogs is extremely challenging due to the highly localized and subtle differences in some specific parts.
Fully Connected Guided Image Filtering
This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF).
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing
In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well.
Image Retargeting by Content-Aware Synthesis
We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators.
Segment-Tree Based Cost Aggregation for Stereo Matching
Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation.
