50 citations · 105 across the 20 of their papers we have counts for
16 papers · 1 filter
PuzzleGPT: Emulating Human Puzzle-Solving Ability for Time and Location Prediction
Hammad Ayyubi, Xuande Feng, Junzhang Liu +3
The task of predicting time and location from images is challenging and requires complex human-like puzzle-solving ability over different clues. In this work, we formalize this abi…
ENTER: Event Based Interpretable Reasoning for VideoQA
Hammad Ayyubi, Junzhang Liu, Ali Asgarov +10
In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where…
JourneyBench: A Challenging One-Stop Vision-Language Understanding Benchmark of Generated Images
Zhecan Wang, Junzhang Liu, Chia-Wei Tang +11
Existing vision-language understanding benchmarks largely consist of images of objects in their usual contexts. As a consequence, recent multimodal large language models can perfor…
HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning
Zhecan Wang, Garrett Bingham, Adams Yu +3
Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal…
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions
Junzhang Liu, Zhecan Wang, Hammad Ayyubi +5
Despite the widespread adoption of Vision-Language Understanding (VLU) benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our analysis reveals a pervasive…
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
Zhecan Wang, Long Chen, Haoxuan You +6
Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions. However, we have identified two dataset biases t…
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…
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…
CoBIT: A Contrastive Bi-directional Image-Text Generation Model
Haoxuan You, Mandy Guo, Zhecan Wang +3
The field of vision and language has witnessed a proliferation of pre-trained foundation models. Most existing methods are independently pre-trained with contrastive objective like…
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…
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…
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,…
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks
Zhecan Wang, Noel Codella, Yen-Chun Chen +7
Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks.…
SGEITL: Scene Graph Enhanced Image-Text Learning for Visual Commonsense Reasoning
Zhecan Wang, Haoxuan You, Liunian Harold Li +5
Answering complex questions about images is an ambitious goal for machine intelligence, which requires a joint understanding of images, text, and commonsense knowledge, as well as…
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…
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
Zhecan Wang, Jian Zhao, Cheng Lu +4
Unconstrained remote gaze estimation remains challenging mostly due to its vulnerability to the large variability in head-pose. Prior solutions struggle to maintain reliable accura…