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Found 35 papers, 13 papers with code
An Analysis of State-of-the-Art Models for Situated Interactive MultiModal Conversations (SIMMC)
There is a growing interest in virtual assistants with multimodal capabilities, e. g., inferring the context of a conversation through scene understanding.
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM
We have developed the \textbf{SnapNTell Dataset}, distinct from traditional VQA datasets: (1) It encompasses a wide range of categorized entities, each represented by images and explicitly named in the answers; (2) It features QA pairs that require extensive knowledge for accurate responses.
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
We also show that by fine-tuning on a small collection of diverse task-oriented dialogues, we can equip modestly sized models, specifically a 13B parameter LLaMA2-Chat model, with function-calling capabilities and DST performance comparable to ChatGPT while maintaining their chat capabilities.
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i. e. text, image, video, audio, IMU motion sensor), and generates textual responses.
Ranked #7 on
Video Question Answering
on STAR Benchmark
Normalized Contrastive Learning for Text-Video Retrieval
Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness.
Navigating Connected Memories with a Task-oriented Dialog System
Recent years have seen an increasing trend in the volume of personal media captured by users, thanks to the advent of smartphones and smart glasses, resulting in large media collections.
Tell Your Story: Task-Oriented Dialogs for Interactive Content Creation
We collect a new dataset C3 (Conversational Content Creation), comprising 10k dialogs conditioned on media montages simulated from a large media collection.
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text
We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with video and text, by projecting them into the joint representation space of Contrastive Language-Image Pre-training (CLIP).
Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks
Searching troves of videos with textual descriptions is a core multimodal retrieval task.
KETOD: Knowledge-Enriched Task-Oriented Dialogue
We also propose two new models, SimpleToDPlus and Combiner, for the proposed task.
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations
Next generation task-oriented dialog systems need to understand conversational contexts with their perceived surroundings, to effectively help users in the real-world multimodal environment.
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue
A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations.
Continual Learning in Task-Oriented Dialogue Systems
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions
As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions.
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
Situated and Interactive Multimodal Conversations
Next generation virtual assistants are envisioned to handle multimodal inputs (e. g., vision, memories of previous interactions, in addition to the user's utterances), and perform multimodal actions (e. g., displaying a route in addition to generating the system's utterance).
User Memory Reasoning for Conversational Recommendation
We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations.
Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity
Assuming a correlation between engagement and user responses such as "liking" messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know.
Memory Graph Networks for Explainable Memory-grounded Question Answering
We introduce Episodic Memory QA, the task of answering personal user questions grounded on memory graph (MG), where episodic memories and related entity nodes are connected via relational edges.
Memory Grounded Conversational Reasoning
To implement such a system, we collect a new corpus of memory grounded conversations, which comprises human-to-human role-playing dialogs given synthetic memory graphs with simulated attributes.
Federated User Representation Learning
Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.
Active Federated Learning
To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency.
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes.
Multimodal Named Entity Disambiguation for Noisy Social Media Posts
We introduce the new Multimodal Named Entity Disambiguation (MNED) task for multimodal social media posts such as Snapchat or Instagram captions, which are composed of short captions with accompanying images.
Multimodal Named Entity Recognition for Short Social Media Posts
We introduce a new task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data such as tweets or Snapchat captions, which comprise short text with accompanying images.
Joint Photo Stream and Blog Post Summarization and Exploration
We alternate between solving the two coupled latent SVM problems, by first fixing the summarization and solving for the alignment from blog images to photo streams and vice versa.
Ranking and Retrieval of Image Sequences From Multiple Paragraph Queries
While most previous work has dealt with the relations between a natural language sentence and an image or a video, our work extends to the relations between paragraphs and image sequences.
Multimodal Transfer Deep Learning with Applications in Audio-Visual Recognition
We propose a transfer deep learning (TDL) framework that can transfer the knowledge obtained from a single-modal neural network to a network with a different modality.
