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Found 12 papers, 7 papers with code
FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding
The development of Long-Context Large Language Models (LLMs) has markedly advanced natural language processing by facilitating the process of textual data across long documents and multiple corpora.
Cross-video Identity Correlating for Person Re-identification Pre-training
For example, compared with the previous state-of-the-art~\cite{ISR}, CION with the same ResNet50-IBN achieves higher mAP of 93. 3\% and 74. 3\% on Market1501 and MSMT17, while only utilizing 8\% training samples.
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models
(3) Financial Practice: whether LLMs can fulfill the practical financial jobs, such as tax consultant, junior accountant and securities analyst.
PanGu-$π$: Enhancing Language Model Architectures via Nonlinearity Compensation
We then demonstrate that the proposed approach is significantly effective for enhancing the model nonlinearity through carefully designed ablations; thus, we present a new efficient model architecture for establishing modern, namely, PanGu-$\pi$.
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Firstly, we construct a new \textbf{dataset} named UFine6926.
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models
Moreover, based on the observation that the accuracy of CLIP model does not increase correspondingly as the parameters of text encoder increase, an extra objective of masked language modeling (MLM) is leveraged for maximizing the potential of the shortened text encoder.
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
Redistribution of Weights and Activations for AdderNet Quantization
To this end, we first thoroughly analyze the difference on distributions of weights and activations in AdderNet and then propose a new quantization algorithm by redistributing the weights and the activations.
Network Amplification With Efficient MACs Allocation
In particular, the proposed method is used to enlarge models sourced by GhostNet, we achieve state-of-the-art 80. 9% and 84. 3% ImageNet top-1 accuracies under the setting of 600M and 4. 4B MACs, respectively.
Dynamic Resolution Network
To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample.
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Based on the observation that many features in SISR models are also similar to each other, we propose to use shift operation to generate the redundant features (i. e., ghost features).
A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem
A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms.
