Search Results for author:
Found 23 papers, 6 papers with code
Toward Faster and Simpler Matrix Normalization via Rank-1 Update
To boost the efficiency in the GPU platform, recent methods rely on Newton-Schulz (NS) iteration to approximate the matrix square-root.
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval
Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on a two-stream structure.
Inflate and Shrink:Enriching and Reducing Interactions for Fast Text-Image Retrieval
Through an inflating operation followed by a shrinking operation, both efficiency and accuracy of a late-interaction model are boosted.
Reconstruct before Query: Continual Missing Modality Learning with Decomposed Prompt Collaboration
Meanwhile, our RebQ leverages extensive multi-modal knowledge from pre-trained LMMs to reconstruct the data of missing modality.
Degenerate Swin to Win: Plain Window-based Transformer without Sophisticated Operations
To bring back the global receptive field, window-based Vision Transformers have devoted a lot of efforts to achieving cross-window communications by developing several sophisticated operations.
R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognition
Recently, vision architectures based exclusively on multi-layer perceptrons (MLPs) have gained much attention in the computer vision community.
Ranked #1 on
3D Object Recognition
on ModelNet40
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models
In particular, we focus on few-shot image recognition tasks on pretrained vision-language models (PVLMs) and develop a method of prompting through prototype (PTP), where we define $K$ image prototypes and $K$ prompt prototypes.
Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning
In this paper, we formulate the APP-installation user embedding learning into a bipartite graph embedding problem.
Boost CTR Prediction for New Advertisements via Modeling Visual Content
To overcome the limitations of behavior ID features in modeling new ads, we exploit the visual content in ads to boost the performance of CTR prediction models.
Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising
How to pair the video ads with the user search is the core task of Baidu video advertising.
BOAT: Bilateral Local Attention Vision Transformer
To improve efficiency, recent Vision Transformers adopt local self-attention mechanisms, where self-attention is computed within local windows.
MVT: Multi-view Vision Transformer for 3D Object Recognition
Nevertheless, multi-view CNN models cannot model the communications between patches from different views, limiting its effectiveness in 3D object recognition.
Ranked #2 on
3D Object Recognition
on ModelNet40
Constructing Orthogonal Convolutions in an Explicit Manner
A convolution layer with an orthogonal Jacobian matrix is 1-Lipschitz in the 2-norm, making the output robust to the perturbation in input.
S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision
More recently, using smaller patches with a pyramid structure, Vision Permutator (ViP) and Global Filter Network (GFNet) achieve better performance than S$^2$-MLP.
Rethinking Token-Mixing MLP for MLP-based Vision Backbone
By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone.
S$^2$-MLP: Spatial-Shift MLP Architecture for Vision
We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration.
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval
Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.
Cross-Probe BERT for Efficient and Effective Cross-Modal Search
Inspired by the great success of BERT in NLP tasks, many text-vision BERT models emerged recently.
Temporal Structure Mining for Weakly Supervised Action Detection
In TSM, each action instance is modeled as a multi-phase process and phase evolving within an action instance, i. e., the temporal structure, is exploited.
Ranked #12 on
Weakly Supervised Action Localization
on ActivityNet-1.3
(mAP@0.5 metric)
Product Quantization Network for Fast Image Retrieval
Product quantization has been widely used in fast image retrieval due to its effectiveness of coding high-dimensional visual features.
Multi-View Harmonized Bilinear Network for 3D Object Recognition
View-based methods have achieved considerable success in $3$D object recognition tasks.
Compressive Quantization for Fast Object Instance Search in Videos
Most of current visual search systems focus on image-to-image (point-to-point) search such as image and object retrieval.
HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos
This paper tackles the problem of efficient and effective object instance search in videos.
