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Found 33 papers, 11 papers with code
SgSum:Transforming Multi-document Summarization into Sub-graph Selection
Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent.
MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction
This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane.
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion
Additionally, we train a warping module to align the hair color with the target region.
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training
Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts.
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge
This report describes the submitted system to the In-Car Multi-Channel Automatic Speech Recognition (ICMC-ASR) challenge, which considers the ASR task with multi-speaker overlapping and Mandarin accent dynamics in the ICMC case.
Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services
The advent of large language models (LLMs) has transformed text-based services, enabling capabilities ranging from real-time translation to AI-driven chatbots.
FedTrans: Efficient Federated Learning via Multi-Model Transformation
Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices.
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion
This paper presents UNIMO-G, a simple multimodal conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs, which demonstrates a unified ability for both text-driven and subject-driven image generation.
Venn: Resource Management Across Federated Learning Jobs
Scheduling edge resources among multiple FL jobs is different from GPU scheduling for cloud ML because of the ephemeral nature and planetary scale of participating devices as well as the overlapping resource requirements of diverse FL jobs.
Efficient Large Language Models: A Survey
We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of efficient LLMs research and inspire them to contribute to this important and exciting field.
3D-Aware Talking-Head Video Motion Transfer
In this paper, we propose a novel 3D-aware talking-head video motion transfer network, Head3D, which fully exploits the subject appearance information by generating a visually-interpretable 3D canonical head from the 2D subject frames with a recurrent network.
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages.
NeRF-Enhanced Outpainting for Faithful Field-of-View Extrapolation
In various applications, such as robotic navigation and remote visual assistance, expanding the field of view (FOV) of the camera proves beneficial for enhancing environmental perception.
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks.
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
We first measure a model's factual robustness by its success rate to defend against adversarial attacks when generating factual information.
Auxo: Efficient Federated Learning via Scalable Client Clustering
In this paper, we explore an additional layer of complexity to mitigate such heterogeneity by grouping clients with statistically similar data distributions (cohorts).
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Diffusion generative models have recently greatly improved the power of text-conditioned image generation.
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.
End-to-end Graph-constrained Vectorized Floorplan Generation with Panoptic Refinement
In the first stage, we encode the room connectivity graph input by users with a graph convolutional network (GCN), then apply an autoregressive transformer network to generate an initial floorplan sequence.
PlaneMVS: 3D Plane Reconstruction from Multi-View Stereo
The semantic plane detection branch is based on a single-view plane detection framework but with differences.
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning
In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training
To bridge the gap between image understanding and generation, we further design a novel commitment loss.
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
In this survey, we provide a systematic overview of the research progress on the faithfulness problem of NLG, including problem analysis, evaluation metrics and optimization methods.
SgSum: Transforming Multi-document Summarization into Sub-graph Selection
Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent.
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
We present FedScale, a federated learning (FL) benchmarking suite with realistic datasets and a scalable runtime to enable reproducible FL research.
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.
Ranked #4 on
Image Captioning
on MS COCO
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries.
An Empirical Study of Propagation-based Methods for Video Object Segmentation
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings.
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates.
