Search Results for author:
Found 22 papers, 14 papers with code
BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learning
To this end, this paper introduces the Balanced and Entropy-based Mix (BEM), a pioneering mixing approach to re-balance the class distribution of both data quantity and uncertainty.
ODM: A Text-Image Further Alignment Pre-training Approach for Scene Text Detection and Spotting
With ODM, we achieve better alignment between text and OCR-Text and enable pre-trained models to adapt to the complex and diverse styles of scene text detection and spotting tasks.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding
Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption.
Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
We propose a model called OTI for ZSVR by employing orthogonal temporal interpolation and the matching loss based on VLMs.
Ranked #1 on
Zero-Shot Action Recognition
on UCF101
EfficientRep:An Efficient Repvgg-style ConvNets with Hardware-aware Neural Network Design
Thus, how to design a neural network to efficiently use the computing ability and memory bandwidth of hardware is a critical problem.
Elastic Aggregation for Federated Optimization
Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients.
Bridging Search Region Interaction With Template for RGB-T Tracking
To alleviate these limitations, we propose a novel Template-Bridged Search region Interaction (TBSI) module which exploits templates as the medium to bridge the cross-modal interaction between RGB and TIR search regions by gathering and distributing target-relevant object and environment contexts.
Ranked #2 on
Rgb-T Tracking
on RGBT210
Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond
In this paper, we introduce a novel Generative Adversarial Networks alike framework, referred to as GAN-MAE, where a generator is used to generate the masked patches according to the remaining visible patches, and a discriminator is employed to predict whether the patch is synthesized by the generator.
Uncertainty-Aware Image Captioning
It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it.
Meta-Ensemble Parameter Learning
Knowledge distillation is an approach that allows a single model to efficiently capture the approximate performance of an ensemble while showing poor scalability as demand for re-training when introducing new teacher models.
Rethinking skip connection model as a learnable Markov chain
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios.
Ranked #14 on
Object Detection
on COCO-O
PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding
To alleviate these drawbacks, we propose a one-stage end-to-end Pixel-Phrase Matching Network (PPMN), which directly matches each phrase to its corresponding pixels instead of region proposals and outputs panoptic segmentation by simple combination.
Efficient Modeling of Future Context for Image Captioning
Existing approaches to image captioning usually generate the sentence word-by-word from left to right, with the constraint of conditioned on local context including the given image and history generated words.
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation
Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos.
Ranked #6 on
Referring Video Object Segmentation
on MeViS
Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation
In inference, we directly use the activation masks from the DA layer as pseudo-labels for segmentation.
Structure Guided Lane Detection
Next, multi-level structural constraints are used to improve the perception of lanes.
Ranked #29 on
Lane Detection
on CULane
Rethinking BiSeNet For Real-time Semantic Segmentation
BiSeNet has been proved to be a popular two-stream network for real-time segmentation.
Ranked #8 on
Real-Time Semantic Segmentation
on Cityscapes test
Large Scale Visual Food Recognition
Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e. g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Large
ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network
To encourage further progress in food recognition, we introduce the dataset ISIA Food- 500 with 500 categories from the list in the Wikipedia and 399, 726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume.
Grand Challenge of 106-Point Facial Landmark Localization
However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.
