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Found 11 papers, 4 papers with code
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective
This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i. e., image classification.
ZeroVL: A Strong Baseline for Aligning Vision-Language Representations with Limited Resources
However, these works require a tremendous amount of data and computational resources (e. g., billion-level web data and hundreds of GPUs), which prevent researchers with limited resources from reproduction and further exploration.
PP-YOLOv2: A Practical Object Detector
To meet these two concerns, we comprehensively evaluate a collection of existing refinements to improve the performance of PP-YOLO while almost keep the infer time unchanged.
OTA: Optimal Transport Assignment for Object Detection
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.
Ranked #50 on
Object Detection
on COCO test-dev
A Reinforcement learning method for Optical Thin-Film Design
Machine learning, especially deep learning, is dramatically changing the methods associated with optical thin-film inverse design.
LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects.
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i. e., post-classification layers) become highly biased towards the negative proposals in the early training stage.
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
To acquire the visible parts, a novel Paired-Box Model (PBM) is proposed to simultaneously predict the full and visible boxes of a pedestrian.
PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression
PS-RCNN first detects slightly/none occluded objects by an R-CNN module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out.
Ranked #2 on
Object Detection
on WiderPerson
A new multilayer optical film optimal method based on deep q-learning
This model can converge the global optimum of the optical thin film structure, this will greatly improve the design efficiency of multi-layer films.
