Search Results for author:
Found 10 papers, 7 papers with code
LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field
In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling.
Instruction Tuning for Secure Code Generation
However, existing instruction tuning schemes overlook a crucial aspect: the security of generated code.
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Large language models (large LMs) are susceptible to producing text that contains hallucinated content.
Hallucination Pair-wise Detection (1-ref)
Informativeness
+2
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Large Language Models for Code: Security Hardening and Adversarial Testing
The task is parametric and takes as input a binary property to guide the LM to generate secure or unsafe code, while preserving the LM's capability of generating functionally correct code.
Text-Guided Mask-free Local Image Retouching
In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning.
On Distribution Shift in Learning-based Bug Detectors
To address this key challenge, we propose to train a bug detector in two phases, first on a synthetic bug distribution to adapt the model to the bug detection domain, and then on a real bug distribution to drive the model towards the real distribution.
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
The problem of fixing errors in programs has attracted substantial interest over the years.
Ranked #1 on
Program Repair
on TFix's Code Patches Data
