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Found 40 papers, 20 papers with code
Direct Unlearning Optimization for Robust and Safe Text-to-Image Models
In this paper, we propose Direct Unlearning Optimization (DUO), a novel framework for removing Not Safe For Work (NSFW) content from T2I models while preserving their performance on unrelated topics.
Geometry-Aware Score Distillation via 3D Consistent Noising and Gradient Consistency Modeling
We demonstrate that our method significantly improves performance, successfully addressing the geometric inconsistency problems in text-to-3D generation task with minimal computation cost and being compatible with existing score distillation-based models.
Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting
3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics.
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Neural Radiance Fields (NeRF) have shown remarkable performance in learning 3D scenes.
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models
While Large Language Models (LLMs) can serve as agents to simulate human behaviors (i. e., role-playing agents), we emphasize the importance of point-in-time role-playing.
Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs
However, it has limitations in capturing low-frequency details and tends to overuse parameters for low-frequency features due to its bias toward fine details, despite its multi-resolution concept.
Active Neural 3D Reconstruction with Colorized Surface Voxel-based View Selection
CSV encapsulates the uncertainty of estimated scene appearance (e. g., color uncertainty) and estimated geometric information (e. g., surface).
Unsupervised Dynamics Prediction with Object-Centric Kinematics
Human perception involves discerning complex multi-object scenes into time-static object appearance (ie, size, shape, color) and time-varying object motion (ie, location, velocity, acceleration).
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Polyhedral Complex Derivation from Piecewise Trilinear Networks
Recent advancements in visualizing deep neural networks provide insights into their structures and mesh extraction from Continuous Piecewise Affine (CPWA) functions.
Dense Text-to-Image Generation with Attention Modulation
To address this, we propose DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle such dense captions while offering control over the scene layout.
Background-aware Moment Detection for Video Moment Retrieval
Video moment retrieval (VMR) identifies a specific moment in an untrimmed video for a given natural language query.
Ranked #9 on
Moment Retrieval
on Charades-STA
Query-Efficient Black-Box Red Teaming via Bayesian Optimization
To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations.
Panoramic Image-to-Image Translation
This task is difficult due to the geometric distortion of panoramic images and the lack of a panoramic image dataset with diverse conditions, like weather or time.
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding.
Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting.
Vision-Language Generative Model for View-Specific Chest X-ray Generation
Synthetic medical data generation has opened up new possibilities in the healthcare domain, offering a powerful tool for simulating clinical scenarios, enhancing diagnostic and treatment quality, gaining granular medical knowledge, and accelerating the development of unbiased algorithms.
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Multi-resolution hash encoding has recently been proposed to reduce the computational cost of neural renderings, such as NeRF.
Semi-Parametric Video-Grounded Text Generation
Efficient video-language modeling should consider the computational cost because of a large, sometimes intractable, number of video frames.
Ranked #22 on
Video Question Answering
on NExT-QA
SelecMix: Debiased Learning by Contradicting-pair Sampling
Experiments on standard benchmarks demonstrate the effectiveness of the method, in particular when label noise complicates the identification of bias-conflicting examples.
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query.
Ranked #2 on
Video Corpus Moment Retrieval
on TVR
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
To combine parameter-efficient adaptation and model compression, we propose AlphaTuning consisting of post-training quantization of the pre-trained language model and fine-tuning only some parts of quantized parameters for a target task.
The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training
As a result, GST scales the amount of training data up to an order of magnitude that of VisDial (1. 2M to 12. 9M QA data).
Conditional Text Generation
Out-of-Distribution Detection
+1
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Based on a recent trend that multimodal generative evaluations exploit a vison-and-language pre-trained model, we propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID).
Ranked #1 on
Human Judgment Classification
on Pascal-50S
Hallucination Pair-wise Detection (1-ref)
Hallucination Pair-wise Detection (4-ref)
+5
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention.
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain.
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation.
Ranked #1 on
Unsupervised Anomaly Detection
on KolektorSDD
(using extra training data)
Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction
When a person identifies objects, he or she can think by associating objects to many classes and conclude by taking inter-class relations into account.
Multi-step Estimation for Gradient-based Meta-learning
To validate our method, we experiment on meta-transfer learning and few-shot learning tasks for multiple settings.
Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks
The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation.
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
Multimodal Dual Attention Memory for Video Story Question Answering
We confirm the best performance of the dual attention mechanism combined with late fusion by ablation studies.
Bilinear Attention Networks
In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly.
Ranked #11 on
Phrase Grounding
on Flickr30k Entities Test
Visual Explanations from Hadamard Product in Multimodal Deep Networks
Kim et al. (2016) show that the Hadamard product in multimodal deep networks, which is well-known for the joint function of visual question answering tasks, implicitly performs an attentional mechanism for visual inputs.
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
The game involves two players: a Teller and a Drawer.
Overcoming Catastrophic Forgetting by Incremental Moment Matching
Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task.
Hadamard Product for Low-rank Bilinear Pooling
Bilinear models provide rich representations compared with linear models.
Multimodal Residual Learning for Visual QA
We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning.
rnn : Recurrent Library for Torch
The rnn package provides components for implementing a wide range of Recurrent Neural Networks.
