513 citations · 1.8k across the 73 of their papers we have counts for
76 papers
The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning
Mingkai Tian, Guorong Li, Yuankai Qi +4
Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem le…
Categorical Keypoint Positional Embedding for Robust Animal Re-Identification
Yuhao Lin, Lingqiao Liu, Javen Shi
Animal re-identification (ReID) has become an indispensable tool in ecological research, playing a critical role in tracking population dynamics, analyzing behavioral patterns, and…
InvariantStock: Learning Invariant Features for Mastering the Shifting Market
Haiyao Cao, Jinan Zou, Yuhang Liu +4
Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution…
Rethinking State Disentanglement in Causal Reinforcement Learning
Haiyao Cao, Zhen Zhang, Panpan Cai +7
One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical supp…
Towards Identifiable Latent Additive Noise Models
Yuhang Liu, Zhen Zhang, Dong Gong +6
Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing…
A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
Yuhao Lin, Haiming Xu, Lingqiao Liu +1
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples. While previous methods achieving this relied on additional trai…
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
Yuhang Liu, Zhen Zhang, Dong Gong +6
Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practic…
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Hamed Damirchi, Cristian Rodríguez-Opazo, Ehsan Abbasnejad +4
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box…
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
Yichao Cai, Yuhang Liu, Zhen Zhang +1
Contrastive vision-language models, such as CLIP, have garnered considerable attention for various downstream tasks, mainly due to the remarkable ability of the learned features fo…
Identifiable Latent Polynomial Causal Models Through the Lens of Change
Yuhang Liu, Zhen Zhang, Dong Gong +5
Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of id…
Influence Function Based Second-Order Channel Pruning-Evaluating True Loss Changes For Pruning Is Possible Without Retraining
Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
A challenge of channel pruning is designing efficient and effective criteria to select channels to prune. A widely used criterion is minimal performance degeneration. To accurately…
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
Modern deep neural networks, particularly recent large language models, come with massive model sizes that require significant computational and storage resources. To enable the de…
A Computational Approach for Mapping Electrochemical Activity of Multi-Principal Element Alloys
Jodie A. Yuwono, Xinyu Li, Tyler D. Doležal +4
Multi principal element alloys (MPEAs) comprise a unique class of metal alloys. MPEAs have been demonstrated to possess several exceptional properties, including, as most relevant…
Semantic Role Labeling Guided Out-of-distribution Detection
Jinan Zou, Maihao Guo, Yu Tian +5
Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications. Previous works identify the out-of-distribution (OOD) instance…
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
Yuhao Lin, Haiming Xu, Lingqiao Liu +2
Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been s…
Stock Market Prediction via Deep Learning Techniques: A Survey
Jinan Zou, Qingying Zhao, Yang Jiao +6
Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comp…
Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense
Bao Gia Doan, Ehsan Abbasnejad, Javen Qinfeng Shi +1
We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network…
Truncated Matrix Power Iteration for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +5
Recovering underlying Directed Acyclic Graph (DAG) structures from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization prob…
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
Yuhang Liu, Zhen Zhang, Dong Gong +5
Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multip…
Identifying Weight-Variant Latent Causal Models
Yuhang Liu, Zhen Zhang, Dong Gong +5
The task of causal representation learning aims to uncover latent higher-level causal variables that affect lower-level observations. Identifying the true latent causal variables f…
Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
Jinan Zou, Haiyao Cao, Lingqiao Liu +3
Natural Language Processing(NLP) demonstrates a great potential to support financial decision-making by analyzing the text from social media or news outlets. In this work, we build…
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…
Implicit Sample Extension for Unsupervised Person Re-Identification
Xinyu Zhang, Dongdong Li, Zhigang Wang +5
Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different…
Active Learning by Feature Mixing
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney +3
The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is e…