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Found 27 papers, 8 papers with code
Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models
To address this issue, we propose a method called StitchDiffusion.
V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network
The V2X-AHD can effectively improve the accuracy of 3D object detection and reduce the number of network parameters, according to this study, which serves as a benchmark for cooperative perception.
Ranked #2 on
3D Object Detection
on V2XSet
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
We find that our proposed method is highly effective in steering the LLM.
Instruction-following Evaluation through Verbalizer Manipulation
We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.
AlpaGasus: Training A Better Alpaca with Fewer Data
Large language models~(LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction.
ESOD:Edge-based Task Scheduling for Object Detection
In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e. g., brightness, saturation).
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
We first present deep probabilistic logic(DPL), which offers a unifying framework for task-specific self-supervision by composing probabilistic logic with deep learning.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Constrained Radar Waveform Design for Range Profiling
Range profiling refers to the measurement of target response along the radar slant range.
Contextual Heterogeneous Graph Network for Human-Object Interaction Detection
However, previous graph models regard human and object as the same kind of nodes and do not consider that the messages are not equally the same between different entities.
Knowledge Efficient Deep Learning for Natural Language Processing
Second, we apply a KRDL model to assist the machine reading models to find the correct evidence sentences that can support their decision.
On-The-Fly Information Retrieval Augmentation for Language Models
Here we experiment with the use of information retrieval as an augmentation for pre-trained language models.
MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning
To facilitate supervised consistency, reliable negative examples are mined from unlabeled data due to the absence of negative samples.
Improving Pre-Trained Multilingual Model with Vocabulary Expansion
However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.
Evidence Sentence Extraction for Machine Reading Comprehension
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks.
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics.
Learning to Globally Edit Images with Textual Description
The models (bucket, filter bank, and end-to-end) differ in how much expert knowledge is encoded, with the most general version being purely end-to-end.
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision
In this paper, we propose deep probabilistic logic (DPL) as a general framework for indirect supervision, by composing probabilistic logic with deep learning.
Emergent Predication Structure in Hidden State Vectors of Neural Readers
A significant number of neural architectures for reading comprehension have recently been developed and evaluated on large cloze-style datasets.
Broad Context Language Modeling as Reading Comprehension
Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015).
Ranked #32 on
Language Modelling
on LAMBADA
Who did What: A Large-Scale Person-Centered Cloze Dataset
We have constructed a new "Who-did-What" dataset of over 200, 000 fill-in-the-gap (cloze) multiple choice reading comprehension problems constructed from the LDC English Gigaword newswire corpus.
Reducing Runtime by Recycling Samples
Contrary to the situation with stochastic gradient descent, we argue that when using stochastic methods with variance reduction, such as SDCA, SAG or SVRG, as well as their variants, it could be beneficial to reuse previously used samples instead of fresh samples, even when fresh samples are available.
Single-View Reconstruction via Joint Analysis of Image and Shape Collections
We present an approach to automatic 3D reconstruction of objects depicted in Web images.
