52 citations · 109 across the 27 of their papers we have counts for
23 papers · 1 filter
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding
Rui Sun, Zhecan Wang, Haoxuan You +3
Vision-language tasks, such as VQA, SNLI-VE, and VCR are challenging because they require the model's reasoning ability to understand the semantics of the visual world and natural…
Learning from Children: Improving Image-Caption Pretraining via Curriculum
Hammad A. Ayyubi, Rahul Lokesh, Alireza Zareian +2
Image-caption pretraining has been quite successfully used for downstream vision tasks like zero-shot image classification and object detection. However, image-caption pretraining…
IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models
Haoxuan You, Rui Sun, Zhecan Wang +5
The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot…
Supervised Masked Knowledge Distillation for Few-Shot Transformers
Han Lin, Guangxing Han, Jiawei Ma +3
Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However,…
DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection
Jiawei Ma, Yulei Niu, Jincheng Xu +3
Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approa…
What, when, and where? -- Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
Brian Chen, Nina Shvetsova, Andrew Rouditchenko +6
Spatio-temporal grounding describes the task of localizing events in space and time, e.g., in video data, based on verbal descriptions only. Models for this task are usually traine…
In Defense of Structural Symbolic Representation for Video Event-Relation Prediction
Andrew Lu, Xudong Lin, Yulei Niu +1
Understanding event relationships in videos requires a model to understand the underlying structures of events (i.e. the event type, the associated argument roles, and correspondin…
Find Someone Who: Visual Commonsense Understanding in Human-Centric Grounding
Haoxuan You, Rui Sun, Zhecan Wang +2
From a visual scene containing multiple people, human is able to distinguish each individual given the context descriptions about what happened before, their mental/physical states…
TempCLR: Temporal Alignment Representation with Contrastive Learning
Yuncong Yang, Jiawei Ma, Shiyuan Huang +4
Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a commo…
Video Event Extraction via Tracking Visual States of Arguments
Guang Yang, Manling Li, Jiajie Zhang +3
Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the…
Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense
Zhecan Wang, Haoxuan You, Yicheng He +3
Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve compr…
Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy
Shiyuan Huang, Robinson Piramuthu, Shih-Fu Chang +1
In Video Question Answering (VideoQA), answering general questions about a video requires its visual information. Yet, video often contains redundant information irrelevant to the…
Weakly-Supervised Temporal Article Grounding
Long Chen, Yulei Niu, Brian Chen +6
Given a long untrimmed video and natural language queries, video grounding (VG) aims to temporally localize the semantically-aligned video segments. Almost all existing VG work hol…
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training
Haoxuan You, Luowei Zhou, Bin Xiao +5
Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a…
Beyond Grounding: Extracting Fine-Grained Event Hierarchies Across Modalities
Hammad A. Ayyubi, Christopher Thomas, Lovish Chum +8
Events describe happenings in our world that are of importance. Naturally, understanding events mentioned in multimedia content and how they are related forms an important way of c…
Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval
Xudong Lin, Simran Tiwari, Shiyuan Huang +4
Multi-channel video-language retrieval require models to understand information from different channels (e.g. videoquestion, videospeech) to correctly link a video with a tex…
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Zhenhailong Wang, Manling Li, Ruochen Xu +10
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question…
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks
Zhecan Wang, Noel Codella, Yen-Chun Chen +8
Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples,…
Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting
Guangxing Han, Long Chen, Jiawei Ma +3
We study multi-modal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection, which are complementary to ea…
Fine-Grained Visual Entailment
Christopher Thomas, Yipeng Zhang, Shih-Fu Chang
Visual entailment is a recently proposed multimodal reasoning task where the goal is to predict the logical relationship of a piece of text to an image. In this paper, we propose a…
CLIP-Event: Connecting Text and Images with Event Structures
Manling Li, Ruochen Xu, Shuohang Wang +6
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing v…
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition
Shiyuan Huang, Jiawei Ma, Guangxing Han +1
We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rej…
Learning Visual Commonsense for Robust Scene Graph Generation
Alireza Zareian, Zhecan Wang, Haoxuan You +1
Scene graph generation models understand the scene through object and predicate recognition, but are prone to mistakes due to the challenges of perception in the wild. Perception e…