Search Results for author:
Found 19 papers, 15 papers with code
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
The multi-aspect categorization of FETV enables fine-grained analysis of the metrics' reliability in different scenarios.
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
The encode-decoder framework has shown recent success in image captioning.
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets.
TempCompass: Do Video LLMs Really Understand Videos?
Motivated by these two problems, we propose the \textbf{TempCompass} benchmark, which introduces a diversity of temporal aspects and task formats.
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage.
ROSITA: Refined BERT cOmpreSsion with InTegrAted techniques
Based on the empirical findings, our best compressed model, dubbed Refined BERT cOmpreSsion with InTegrAted techniques (ROSITA), is $7. 5 \times$ smaller than BERT while maintains $98. 5\%$ of the performance on five tasks of the GLUE benchmark, outperforming the previous BERT compression methods with similar parameter budget.
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training
Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
The ability to perceive how objects change over time is a crucial ingredient in human intelligence.
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples).
COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models
Motivated by such considerations, we propose a collaborative optimization for PLMs that integrates static model compression and dynamic inference acceleration.
Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
To this end, we systematically study the design of a training and compression pipeline to search the subnetworks, as well as the assignment of sparsity to different modality-specific modules.
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation
In this paper, however, we observe that although distilling the teacher's hidden state knowledge (HSK) is helpful, the performance gain (marginal utility) diminishes quickly as more HSK is distilled.
Self-Adaptive Scaling for Learnable Residual Structure
Residual has been widely applied to build deep neural networks with enhanced feature propagation and improved accuracy.
Ranking and Sampling in Open-Domain Question Answering
Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs.
Unsupervised Pre-training for Natural Language Generation: A Literature Review
They are classified into architecture-based methods and strategy-based methods, based on their way of handling the above obstacle.
Exploring and Distilling Cross-Modal Information for Image Captioning
Recently, attention-based encoder-decoder models have been used extensively in image captioning.
Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries.
