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Found 28 papers, 20 papers with code
Diffusion Facial Forgery Detection
In particular, this dataset leverages 30, 000 carefully collected textual and visual prompts, ensuring the synthesis of images with both high fidelity and semantic consistency.
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large Models
Teaching Visual Question Answering (VQA) models to refrain from answering unanswerable questions is necessary for building a trustworthy AI system.
PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks.
ELIP: Efficient Language-Image Pre-training with Fewer Vision Tokens
Learning a versatile language-image model is computationally prohibitive under a limited computing budget.
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years.
Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets.
Towards Generalizable Deepfake Detection by Primary Region Regularization
The existing deepfake detection methods have reached a bottleneck in generalizing to unseen forgeries and manipulation approaches.
Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection
Human-Object Interaction Detection is a crucial aspect of human-centric scene understanding, with important applications in various domains.
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
The RL-GP adopts the ensemble population strategies.
Learning to Agree on Vision Attention for Visual Commonsense Reasoning
Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning.
Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection
To quantitatively study the object bias problem, we advocate a new protocol for evaluating model performance.
Distance Matters in Human-Object Interaction Detection
2) Insufficient number of distant interactions in benchmark datasets results in under-fitting on these instances.
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
On the other hand, pertaining to the implicit knowledge, the multi-modal implicit knowledge for knowledge-based VQA still remains largely unexplored.
Voice-Face Homogeneity Tells Deepfake
To this end, a voice-face matching method is devised to measure the matching degree of these two.
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning
Logical reasoning is of vital importance to natural language understanding.
Ranked #3 on
Reading Comprehension
on ReClor
Joint Answering and Explanation for Visual Commonsense Reasoning
Given that our framework is model-agnostic, we apply it to the existing popular baselines and validate its effectiveness on the benchmark dataset.
On Modality Bias Recognition and Reduction
From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Attention mechanisms are dominating the explainability of deep models.
When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
In addition, we empirically prove that collaborative filtering and semantic matching are complementary to each other in product search performance enhancement.
Review Polarity-wise Recommender
To be specific, in this model, positive and negative reviews are separately gathered and utilized to model the user-preferred and user-rejected aspects, respectively.
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years.
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Experimental results demonstrate that our adapted margin cosine loss can greatly enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach
This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i. e., the selected answer span is inappropriate for question generation.
Feature-level Attentive ICF for Recommendation
To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models.
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach
However, recent studies have pointed out that the highlighted image regions from the visual attention are often irrelevant to the given question and answer, leading to model confusion for correct visual reasoning.
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
Concretely, we design a novel interpretation scheme whereby the loss of mis-predicted frequent and sparse answers of the same question type is distinctly exhibited during the late training phase.
Enhancing Factorization Machines with Generalized Metric Learning
Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors.
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering
Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years.
