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Found 13 papers, 8 papers with code
OV-PARTS: Towards Open-Vocabulary Part Segmentation
Secondly, part segmentation introduces an open granularity challenge due to the diverse and often ambiguous definitions of parts in the open world.
Understanding Masked Autoencoders From a Local Contrastive Perspective
Masked AutoEncoder (MAE) has revolutionized the field of self-supervised learning with its simple yet effective masking and reconstruction strategies.
In Defense of Clip-based Video Relation Detection
While recent video-based methods utilizing video tubelets have shown promising results, we argue that the effective modeling of spatial and temporal context plays a more significant role than the choice between clip tubelets and video tubelets.
Rethinking the Two-Stage Framework for Grounded Situation Recognition
Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage.
Ranked #3 on
Situation Recognition
on imSitu
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction.
Vision Transformer with Progressive Sampling
As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.
Visual Parser: Representing Part-whole Hierarchies with Transformers
To model the representations of the two levels, we first encode the information from the whole into part vectors through an attention mechanism, then decode the global information within the part vectors back into the whole representation.
Ranked #313 on
Image Classification
on ImageNet
Spatial Dual-Modality Graph Reasoning for Key Information Extraction
In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild.
Aggregation With Feature Detection
Aggregating features from different depths of a network is widely adopted to improve the network capability.
HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation
Second, since learning too many context-specific classification subspaces can suffer from data sparsity issues, we propose a hierarchical semantic aggregation(HSA) module to reduces the number of subspaces by introducing higher order structural information.
RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
Theoretically, our proposed method, dubbed \emph{RobustScanner}, decodes individual characters with dynamic ratio between context and positional clues, and utilizes more positional ones when the decoding sequences with scarce context, and thus is robust and practical.
Geometry Normalization Networks for Accurate Scene Text Detection
Large geometry (e. g., orientation) variances are the key challenges in the scene text detection.
Ranked #10 on
Scene Text Detection
on ICDAR 2017 MLT
Boosting up Scene Text Detectors with Guided CNN
Deep CNNs have achieved great success in text detection.
