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Found 14 papers, 9 papers with code
DevBench: A Comprehensive Benchmark for Software Development
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities.
Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning
We find that Hawkeye is able to generate GUI event sequences targeting changed functions more reliably than FastBot2 and ARES for the open source Apps and the large commercial App.
CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
CFNS Ad-Hoc meeting on Radiative Corrections Whitepaper
Current precision scattering experiments and even more so many experiments planed for the Electron Ion Collider will be limited by systematics.
Nuclear Theory
How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study
The \emph{second contribution} is that we systematically evaluated five benchmark methods on two representative public datasets.
An End-to-End Network for Panoptic Segmentation
Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic.
DetNet: Design Backbone for Object Detection
(1) Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales.
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Semantic segmentation requires both rich spatial information and sizeable receptive field.
Ranked #4 on
Semantic Segmentation
on SkyScapes-Dense
Dichotomous Image Segmentation
Real-Time Semantic Segmentation
+2
Learning a Discriminative Feature Network for Semantic Segmentation
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction.
Ranked #5 on
Semantic Segmentation
on PASCAL VOC 2012 test
DetNet: A Backbone network for Object Detection
Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection.
ExFuse: Enhancing Feature Fusion for Semantic Segmentation
Modern semantic segmentation frameworks usually combine low-level and high-level features from pre-trained backbone convolutional models to boost performance.
Ranked #4 on
Semantic Segmentation
on PASCAL VOC 2012 val
(using extra training data)
Light-Head R-CNN: In Defense of Two-Stage Object Detector
More importantly, simply replacing the backbone with a tiny network (e. g, Xception), our Light-Head R-CNN gets 30. 7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy.
MegDet: A Large Mini-Batch Object Detector
The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design.
Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
One of recent trends [30, 31, 14] in network architec- ture design is stacking small filters (e. g., 1x1 or 3x3) in the entire network because the stacked small filters is more ef- ficient than a large kernel, given the same computational complexity.
Ranked #8 on
Semantic Segmentation
on PASCAL VOC 2012 val
