Search Results for author:
Found 24 papers, 12 papers with code
TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning.
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection
Hateful memes have become a significant concern on the Internet, necessitating robust automated detection systems.
Ranked #1 on
Hateful Meme Classification
on Hateful Memes
CULTURE3D: Cultural Landmarks and Terrain Dataset for 3D Applications
The dataset enables seamless integration with multi-modal data, supporting a range of 3D applications, from architectural reconstruction to virtual tourism.
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding
Leveraging the advanced text processing capabilities of large language models (LLMs), X-LeBench develops a life-logging simulation pipeline that produces realistic, coherent daily plans aligned with real-world video data.
Lucia: A Temporal Computing Platform for Contextual Intelligence
The rapid evolution of artificial intelligence, especially through multi-modal large language models, has redefined user interactions, enabling responses that are contextually rich and human-like.
Human-inspired Perspectives: A Survey on AI Long-term Memory
With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant.
On Extending Direct Preference Optimization to Accommodate Ties
We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons.
Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata
The Directed Acyclic Transformer is a fast non-autoregressive (NAR) model that performs well in Neural Machine Translation.
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches
Two approaches have emerged to input images into large language models (LLMs).
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions.
Ranked #1 on
Retrieval
on InfoSeek
(using extra training data)
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding
Minimum Bayes Risk (MBR) decoding can significantly improve translation performance of Multilingual Large Language Models (MLLMs).
Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning
Hateful memes have emerged as a significant concern on the Internet.
Ranked #2 on
Meme Classification
on MultiOFF
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
FLMR addresses two major limitations in RA-VQA's retriever: (1) the image representations obtained via image-to-text transforms can be incomplete and inaccurate and (2) relevance scores between queries and documents are computed with one-dimensional embeddings, which can be insensitive to finer-grained relevance.
Ranked #1 on
Retrieval
on OK-VQA
Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns
Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata.
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering
The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer.
Schema-Guided Semantic Accuracy: Faithfulness in Task-Oriented Dialogue Response Generation
We show that SGSAcc can be applied to evaluate utterances generated from a wide range of dialogue actions in the Schema Guided Dialogue (SGD) dataset with good agreement with human judgment.
Retrieval Augmented Visual Question Answering with Outside Knowledge
The strong retrieval ability of our model significantly reduces the number of retrieved documents needed in training, yielding significant benefits in answer quality and computation required for training.
Ranked #2 on
Retrieval
on OK-VQA
Transformer-Empowered Content-Aware Collaborative Filtering
Knowledge graph (KG) based Collaborative Filtering is an effective approach to personalizing recommendation systems for relatively static domains such as movies and books, by leveraging structured information from KG to enrich both item and user representations.
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking
Dialogue State Tracking is central to multi-domain task-oriented dialogue systems, responsible for extracting information from user utterances.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning
Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.
Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences
While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness.
Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress
Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse.
Ranked #1 on
Anxiety Detection
on Well-being Dataset
Author2Vec: A Framework for Generating User Embedding
Online forums and social media platforms provide noisy but valuable data every day.
No, you're not alone: A better way to find people with similar experiences on Reddit
This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts.
