Search Results for author:
Found 31 papers, 23 papers with code
Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection
Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations.
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL
Large Language Models (LLMs) have shown promising performance in text-to-SQL, which involves translating natural language questions into SQL queries.
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) is an important sentiment analysis task, which aims to determine the sentiment polarity towards an aspect in a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+6
RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning
The C2FG module aims to seamlessly integrate the side prediction from the previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions.
Ranked #15 on
Change Detection
on WHU-CD
Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems
To this end, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to improve the LLMs' math problem-solving ability by addressing semantic misunderstanding errors.
Ranked #1 on
Math Word Problem Solving
on SVAMP
(Accuracy metric)
When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability
Subsequently, to enhance controllability with inexplicit masks, an advanced Shape-aware ControlNet consisting of a deterioration estimator and a shape-prior modulation block is devised.
ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding
With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical.
Revisiting Knowledge Distillation for Autoregressive Language Models
Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model.
Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation
We use this TS model to iteratively generate the pixel-level text labels in a semi-automatical manner, unifying labels across the four text hierarchies in the HierText dataset.
Ranked #1 on
Hierarchical Text Segmentation
on HierText
Hierarchical Text Segmentation
parameter-efficient fine-tuning
+2
GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching
In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance.
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models
The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem.
PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions
Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels.
DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting
In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single decoder with explicit points solo for text detection, recognition, and script identification simultaneously.
Ranked #1 on
Text Spotting
on Inverse-Text
Revisiting Token Dropping Strategy in Efficient BERT Pretraining
Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers.
Self-Evolution Learning for Discriminative Language Model Pretraining
Masked language modeling, widely used in discriminative language model (e. g., BERT) pretraining, commonly adopts a random masking strategy.
Scalable Mask Annotation for Video Text Spotting
Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames.
Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT
Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries.
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE
This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference.
Diff-Font: Diffusion Model for Robust One-Shot Font Generation
Specifically, a large stroke-wise dataset is constructed, and a stroke-wise diffusion model is proposed to preserve the structure and the completion of each generated character.
Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE
This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard.
Ranked #1 on
Common Sense Reasoning
on ReCoRD
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting
In this paper, we present DeepSolo, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously.
Ranked #1 on
Text Spotting
on Total-Text
(using extra training data)
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models
Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization.
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation
Prompt Transfer (PoT) is a recently-proposed approach to improve prompt-tuning, by initializing the target prompt with the existing prompt trained on similar source tasks.
DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation.
Ranked #3 on
Scene Text Detection
on SCUT-CTW1500
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation
To verify our hypothesis, we first empirically study the functionalities of the encoder and decoder in seq2seq pretrained language models, and find that the encoder takes an important but under-exploitation role than the decoder regarding the downstream performance and neuron activation.
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.
Change Detection
Change detection for remote sensing images
+1
Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis
To this end, we propose a knowledge graph augmented network KGAN, which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.
Ranked #10 on
Scene Text Recognition
on ICDAR2015
(using extra training data)
Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis
In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model, respectively.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection
Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.
Ranked #5 on
Scene Text Detection
on SCUT-CTW1500
TextFuseNet: Scene Text Detection with Richer Fused Features
More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.
Ranked #1 on
Scene Text Detection
on ICDAR 2015
