Search Results for author:
Found 18 papers, 11 papers with code
CPN: Complementary Proposal Network for Unconstrained Text Detection
Existing methods for scene text detection can be divided into two paradigms: segmentation-based and anchor-based.
Center Contrastive Loss for Metric Learning
In this paper, we propose a novel metric learning function called Center Contrastive Loss, which maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss.
Ranked #5 on
Metric Learning
on CUB-200-2011
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios.
Both Spatial and Frequency Cues Contribute to High-Fidelity Image Inpainting
Specifically, we introduce an extra Frequency Branch and Frequency Loss on the spatial-based network to impose direct supervision on the frequency information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to fuse multi-domain features and combine the corresponding characteristics.
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
Contrastive learning-based video-language representation learning approaches, e. g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs.
Ranked #8 on
Video Question Answering
on MSRVTT-QA
RepGhost: A Hardware-Efficient Ghost Module via Re-parameterization
Experiments on ImageNet and COCO benchmarks demonstrate that the proposed RepGhostNet is much more effective and efficient than GhostNet and MobileNetV3 on mobile devices.
TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval
In this paper, we propose Token Shift and Selection Network (TS2-Net), a novel token shift and selection transformer architecture, which dynamically adjusts the token sequence and selects informative tokens in both temporal and spatial dimensions from input video samples.
Ranked #8 on
Video Retrieval
on VATEX
Aesthetic Text Logo Synthesis via Content-aware Layout Inferring
To train and evaluate our approach, we construct a dataset named as TextLogo3K, consisting of about 3, 500 text logo images and their pixel-level annotations.
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow
Recently, the dense correlation volume method achieves state-of-the-art performance in optical flow.
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress.
CLIP2Video: Mastering Video-Text Retrieval via Image CLIP
We present CLIP2Video network to transfer the image-language pre-training model to video-text retrieval in an end-to-end manner.
Ranked #11 on
Video Retrieval
on VATEX
(using extra training data)
Practical Wide-Angle Portraits Correction with Deep Structured Models
Wide-angle portraits often enjoy expanded views.
Local Context Attention for Salient Object Segmentation
Based on this priori, we propose a novel Local Context Attention Network (LCANet) to generate locally reinforcement feature maps in a uniform representational architecture.
TP-LSD: Tri-Points Based Line Segment Detector
To realize one-step detection with a faster and more compact model, we introduce the tri-points representation, converting the line segment detection to the end-to-end prediction of a root-point and two endpoints for each line segment.
Ranked #2 on
Line Segment Detection
on York Urban Dataset
Affinity-aware Compression and Expansion Network for Human Parsing
As a fine-grained segmentation task, human parsing is still faced with two challenges: inter-part indistinction and intra-part inconsistency, due to the ambiguous definitions and confusing relationships between similar human parts.
Deep Fusion Network for Image Completion
The fusion block not only provides a smooth fusion between restored and existing content, but also provides an attention map to make network focus more on the unknown pixels.
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints.
Ranked #7 on
SMAC+
on Def_Infantry_parallel
Pyramid Attention Network for Semantic Segmentation
A Pyramid Attention Network(PAN) is proposed to exploit the impact of global contextual information in semantic segmentation.
