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Found 13 papers, 7 papers with code
OmniBal: Towards Fast Instruct-tuning for Vision-Language Models via Omniverse Computation Balance
We rebalanced the computational loads from data, model, and memory perspectives to address this issue, achieving more balanced computation across devices.
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension
Our experimental results reveal that following this pretraining, both Code Llama and StarCoder, the prevalent code domain pretraining models, display significant improvements on our logically equivalent code selection task and the code completion task.
2023 Low-Power Computer Vision Challenge (LPCVC) Summary
This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC).
Rethinking the Instruction Quality: LIFT is What You Need
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data.
SUT: Active Defects Probing for Transcompiler Models
In this paper we introduce a new metrics for programming language translation and these metrics address these basic syntax errors.
Program Translation via Code Distillation
Distilled code serves as a translation pivot for any programming language, leading by construction to parallel corpora which scale to all available source code by simply applying the distillation compiler.
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different system implementations.
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative Learning
Specifically, there are an object detection task (consisting of an instance-classification task and a localization task) and an image-classification task in our framework, responsible for utilizing the two types of supervision.
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition
Long-tail distribution is widely spread in real-world applications.
Towards Frame Rate Agnostic Multi-Object Tracking
In this paper, we propose a Frame Rate Agnostic MOT framework with a Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time.
Equalized Focal Loss for Dense Long-Tailed Object Detection
The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem.
Cross-dataset Training for Class Increasing Object Detection
Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets.
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.
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Object Detection
on COCO-O
