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Found 11 papers, 6 papers with code
CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
For each of these CRUD categories, we have developed comprehensive datasets to evaluate the performance of RAG systems.
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models
We further extend our method to a novel image editing task: substituting the subject in an image through textual manipulations.
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
These techniques encompass the use of directed hallucination induction and strategies that deliberately alter authentic text to produce hallucinations.
UnLoc: A Unified Framework for Video Localization Tasks
While large-scale image-text pretrained models such as CLIP have been used for multiple video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos is still a relatively unexplored task.
Ranked #1 on
Action Segmentation
on COIN
MediaGPT : A Large Language Model For Chinese Media
Large language models (LLMs) have shown remarkable capabilities in generating high-quality text and making predictions based on large amounts of data, including the media domain.
Feudal Reinforcement Learning by Reading Manuals
The manager agent is a multi-hop plan generator dealing with high-level abstract information and generating a series of sub-goals in a backward manner.
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e. g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models.
Interpretable Visual Reasoning via Induced Symbolic Space
Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space.
Ranked #3 on
Visual Question Answering (VQA)
on CLEVR
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.
Ranked #8 on
Semantic Segmentation
on DensePASS
Clothing Retrieval with Visual Attention Model
The VAM is further connected to a global network to form an end-to-end network structure through Impdrop connection which randomly Dropout on the feature maps with the probabilities given by the attention map.
