Search Results for author:
Found 37 papers, 24 papers with code
AlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
(1) Mutually-Refined Proposal Extraction.
DEADiff: An Efficient Stylization Diffusion Model with Disentangled Representations
The Q-Formers are trained using paired images rather than the identical target, in which the reference image and the ground-truth image are with the same style or semantics.
Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
In this paper, we introduce Modal-Enhanced Semantic Modeling (MESM), a novel framework for more balanced alignment through enhancing features at two levels.
CARIS: Context-Augmented Referring Image Segmentation
Technically, CARIS develops a context-aware mask decoder with sequential bidirectional cross-modal attention to integrate the linguistic features with visual context, which are then aligned with pixel-wise visual features.
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval
Hence, we address this problem by decomposing video information into reconstruction-dependent and semantic-dependent information, which disentangles the semantic extraction from reconstruction constraint.
Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition
In this paper, we explore the potential of the Contrastive Language-Image Pretraining (CLIP) model in scene text recognition (STR), and establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) to leverage both visual and linguistic knowledge in CLIP.
Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction
This implies the great potential of the semantic correlations for the entity-independent inductive link prediction task.
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design
Specifically, TextPainter takes the global-local background image as a hint of style and guides the text image generation with visual harmony.
Balanced Classification: A Unified Framework for Long-Tailed Object Detection
In this paper, we contend that the learning bias originates from two factors: 1) the unequal competition arising from the imbalanced distribution of foreground categories, and 2) the lack of sample diversity in tail categories.
Ranked #1 on
Long-tailed Object Detection
on LVIS v1.0 val
MomentDiff: Generative Video Moment Retrieval from Random to Real
Video moment retrieval pursues an efficient and generalized solution to identify the specific temporal segments within an untrimmed video that correspond to a given language description.
TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
In this work, we introduce TPS++, an attention-enhanced TPS transformation that incorporates the attention mechanism to text rectification for the first time.
Ranked #1 on
Scene Text Recognition
on SVT-P
Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition
Vision model have gained increasing attention due to their simplicity and efficiency in Scene Text Recognition (STR) task.
Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation
POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype.
Exploring Stroke-Level Modifications for Scene Text Editing
Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets.
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input.
Ranked #4 on
Text Spotting
on SCUT-CTW1500
Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets
On channel aspect, we introduce a dynamic feature aggregation module in MLP and a brand new "head token" design in multi-head self-attention module to help re-calibrate channel representation and make different channel group representation interacts with each other.
Geometry Aligned Variational Transformer for Image-conditioned Layout Generation
First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images.
REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer
Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person.
Partial Class Activation Attention for Semantic Segmentation
Current attention-based methods for semantic segmentation mainly model pixel relation through pairwise affinity and coarse segmentation.
CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
In this paper, we propose a novel module called Multi-Domain Character Distance Perception (MDCDP) to establish a visually and semantically related position embedding.
Ranked #11 on
Scene Text Recognition
on ICDAR2015
From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
Such operation guides the vision model to use not only the visual texture of characters, but also the linguistic information in visual context for recognition when the visual cues are confused (e. g. occlusion, noise, etc.).
Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition
To tackle this issue, in this paper, we propose a dual parallel attention network (DPAN), in which a newly designed parallel context attention module (PCAM) is cascaded with the original PPAM, using linguistic contextual information to compensate for the information inconsistency between queries and keys.
Ranked #12 on
Scene Text Recognition
on ICDAR2013
PERT: A Progressively Region-based Network for Scene Text Removal
However, there exists two problems: 1) the implicit erasure guidance causes the excessive erasure to non-text areas; 2) the one-stage erasure lacks the exhaustive removal of text region.
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning
Cross-modal correlation provides an inherent supervision for video unsupervised representation learning.
Memory Enhanced Embedding Learning for Cross-Modal Video-Text Retrieval
The cross-modal memory module is employed to record the instance embeddings of all the datasets for global negative mining.
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries.
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively.
Hierarchical Granularity Transfer Learning
In this paper, we introduce a new task, named Hierarchical Granularity Transfer Learning (HGTL), to recognize sub-level categories with basic-level annotations and semantic descriptions for hierarchical categories.
Curriculum Learning for Natural Language Understanding
With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks.
ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection
Then a novel Local Orthogonal Texture-aware Module (LOTM) models the local texture information of proposal features in two orthogonal directions and represents text region with a set of contour points.
Graph Structured Network for Image-Text Matching
The GSMN explicitly models object, relation and attribute as a structured phrase, which not only allows to learn correspondence of object, relation and attribute separately, but also benefits to learn fine-grained correspondence of structured phrase.
Ranked #16 on
Cross-Modal Retrieval
on Flickr30k
Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem.
ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths
Nowadays U-net-like FCNs predominate various biomedical image segmentation applications and attain promising performance, largely due to their elegant architectures, e. g., symmetric contracting and expansive paths as well as lateral skip-connections.
Domain-Specific Embedding Network for Zero-Shot Recognition
In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains.
CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification
An appearance network is developed to learn appearance features from the full body, horizontal and vertical body parts of pedestrians with spatial dependencies among body parts.
