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Jie Fu

Alumni

Publications

Graph Neural Networks Meet Probabilistic Graphical Models: A Survey
Chenqing Hua
Sitao Luan
Qian Zhang
Jie Fu
Guy Wolf
2025-04-06
IEEE International Conference on Acoustics, Speech, and Signal Processing (publié)
doi.org
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Li Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
Model merging has emerged as an effective approach to combine multiple single-task models, fine-tuned from the same pre-trained model, into … (voir plus)a multitask model. This process typically involves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during model merging. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP identifies a Pareto set of scaling coefficients for merging multiple models to reflect the trade-offs. The core component of MAP is approximating the evaluation metrics of the various tasks using a quadratic approximation surrogate model derived from a pre-selected set of scaling coefficients, enabling amortized inference. Experimental results on vision and natural language processing tasks show that MAP can accurately identify the Pareto front. To further reduce the required computation of MAP, we propose (1) a Bayesian adaptive sampling algorithm and (2) a nested merging scheme with multiple stages.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
VCR: Pixel-Level Complex Reasoning by Restoring Occluded Text
Tianyu Zhang
Suyuchen Wang
Li Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images through complex reasoning. This task stems from the observation that text embedded in images intrinsically differs from common visual elements and text due to the need to align the modalities of vision, text, and text embedded in images. While many works incorporate text into images for visual question answering, they mostly rely on OCR or masked language modeling, reducing the task to text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny, exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct VCR-WIKI for VCR using Wikipedia images with captions, including 2.11M English and 346K Chinese training entities, plus 5K validation and 5K test entities in both languages, each in easy and hard configurations. We also make a hidden test set, VCR-HIDDEN, to avoid potential overfitting on VCR-WIKI. Our results reveal that current vision-language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-WIKI and the data construction code to facilitate future research.
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text
Tianyu Zhang
Suyuchen Wang
Lu Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images through complex reasoning. This task stems from the observation that text embedded in images intrinsically differs from common visual elements and text due to the need to align the modalities of vision, text, and text embedded in images. While many works incorporate text into images for visual question answering, they mostly rely on OCR or masked language modeling, reducing the task to text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny, exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct VCR-WIKI for VCR using Wikipedia images with captions, including 2.11M English and 346K Chinese training entities, plus 5K validation and 5K test entities in both languages, each in easy and hard configurations. We also make a hidden test set, VCR-HIDDEN, to avoid potential overfitting on VCR-WIKI. Our results reveal that current vision-language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-WIKI and the data construction code to facilitate future research.
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
VCR: A Task for Pixel-Level Complex Reasoning in Vision Language Models via Restoring Occluded Text
Tianyu Zhang
Suyuchen Wang
Li Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
VCR: Visual Caption Restoration
Tianyu Zhang
Suyuchen Wang
Li Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.
2024-10-09
NeurIPS.cc/2024/Workshop/Sys2-Reasoning (poster)
doi.org
openreview.net
MAP: Model Merging with Amortized Pareto Front Using Limited Computation
Li Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
2024-10-01
NeurIPS.cc/2024/Workshop/Federated_Learning (présentation orale)
openreview.net
openreview.net
Think Before You Act: Decision Transformers with Working Memory
Jikun Kang
Romain Laroche
Xingdi Yuan
Adam Trischler
Xue (Steve) Liu
Jie Fu
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance rel… (voir plus)ies on massive data and computation. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training. As a result, training on a new task may deteriorate the model’s performance on previous tasks. In contrast to LLMs’ implicit memory mechanism, the human brain utilizes distributed memory storage, which helps manage and organize multiple skills efficiently, mitigating the forgetting phenomenon. Inspired by this, we propose a working memory module to store, blend, and retrieve information for different downstream tasks. Evaluation results show that the proposed method improves training efficiency and generalization in Atari games and Meta-World object manipulation tasks. Moreover, we demonstrate that memory fine-tuning further enhances the adaptability of the proposed architecture.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Lu Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
Model merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically inv… (voir plus)olves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during the merging process. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP efficiently identifies a Pareto set of scaling coefficients for merging multiple models, reflecting the trade-offs involved. It amortizes the substantial computational cost of evaluations needed to estimate the Pareto front by using quadratic approximation surrogate models derived from a pre-selected set of scaling coefficients. Experimental results on vision and natural language processing tasks demonstrate that MAP can accurately identify the Pareto front, providing practitioners with flexible solutions to balance competing task objectives. We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
2024-06-11
ArXiv (prépublication)
doi.org
arxiv.org
VCR: Visual Caption Restoration
Tianyu Zhang
Suyuchen Wang
Lu Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.
2024-06-10
ArXiv (prépublication)
doi.org
arxiv.org
VCR: Visual Caption Restoration
Tianyu Zhang
Suyuchen Wang
Lu Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.
2024-06-10
ArXiv (prépublication)
doi.org
arxiv.org
VCR: Visual Caption Restoration
Tianyu Zhang
Suyuchen Wang
Lu Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
Bang Liu
Yoshua Bengio
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (voir plus)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.
2024-06-10
ArXiv (prépublication)
doi.org
arxiv.org