OpenRaiser/Intern-Atlas huggingface dataset:
From pixels to physics: Probabilistic color de-rendering
Authors: Ying Xiong, Kate Saenko, Trevor Darrell, Todd E. Zickler
Venue: Conference CVPR 2012
Year: 2012

Consumer digital cameras use tone-mapping to produce compact, narrow-gamut images that are nonetheless visually pleasing. In doing so, they discard or distort substantial radiometric signal that could otherwise be used for computer vision. Existing methods attempt to undo these effects through deterministic maps that de-render the reported narrow-gamut colors back to their original wide-gamut sensor measurements. Deterministic approaches are unreliable, however, because the reverse narrow-to-wide mapping is one-to-many and has inherent uncertainty. Our solution is to use probabilistic maps, providing uncertainty estimates useful to many applications. We use a non-parametric Bayesian regression technique - local Gaussian process regression - to learn for each pixel's narrow-gamut color a probability distribution over the scene colors that could have created it. Using a variety of consumer cameras we show that these distributions, once learned from training data, are effective in simple probabilistic adaptations of two popular applications: multi-exposure imaging and photometric stereo. Our results on these applications are better than those of corresponding deterministic approaches, especially for saturated and out-of-gamut colors.
Reconstructing high quality face-surfaces using model based stereo
Authors: Brian Amberg, Andrew Blake 0001, Andrew W. Fitzgibbon, Sami Romdhani, Thomas Vetter
Venue: Conference ICCV 2007
Year: 2007

We present a novel model based stereo system, which accurately extracts the 3D shape and pose of faces from multiple images taken simultaneously. Extracting the 3D shape from images is important in areas such as pose-invariant face recognition and image manipulation. The method is based on a 3D morphable face model learned from a database of facial scans. The use of a strong face prior allows us to extract high precision surfaces from stereo data of faces, where traditional correlation based stereo methods fail because of the mostly textureless input images. The method uses two or more uncalibrated images of arbitrary baseline, estimating calibration and shape simultaneously. Results using two and three input images are presented. We replace the lighting and albedo estimation of a monocular method with the use of stereo information, making the system more accurate and robust. We evaluate the method using ground truth data and the standard PIE image dataset. A comparison with the state of the art monocular system shows that the new method has a significantly higher accuracy.
What can missing correspondences tell us about 3d structure and motion?
Authors: Christopher Zach, Arnold Irschara, Horst Bischof
Venue: Conference CVPR 2008
Year: 2008

Practically all existing approaches to structure and motion computation use only positive image correspondences to verify the camera pose hypotheses. Incorrect epipolar geometries are solely detected by identifying outliers among the found correspondences. Ambiguous patterns in the images are often incorrectly handled by these standard methods. In this work we propose two approaches to overcome such problems. First, we apply non-monotone reasoning on view triplets using a Bayesian formulation. In contrast to two-view epipolar geometry, image triplets allow the prediction of features in the third image. Absence of these features (i.e. missing correspondences) enables additional inference about the view triplet. Furthermore, we integrate these view triplet handling into an incremental procedure for structure and motion computation. Thus, our approach is able to refine the maintained 3D structure when additional image data is provided.
Search behavior prediction: A hypergraph perspective
Authors: Yan Han, Edward W. Huang, Wenqing Zheng, Nikhil Rao, Zhangyang Wang, Karthik Subbian
Venue: Conference WSDM 2023
Year: 2023

At E-Commerce stores such as Amazon, eBay, and Taobao, the shopping items and the query words that customers use to search for the items form a bipartite graph that captures search behavior. Such a query-item graph can be used to forecast search trends or improve search results. For example, generating query-item associations, which is equivalent to predicting links in the bipartite graph, can yield recommendations that can customize and improve the user search experience. Although the bipartite shopping graphs are straightforward to model search behavior, they suffer from two challenges: 1) The majority of items are sporadically searched and hence have noisy/sparse query associations, leading to a long-tail distribution. 2) Infrequent queries are more likely to link to popular items, leading to another hurdle known as disassortative mixing. To address these two challenges, we go beyond the bipartite graph to take a hypergraph perspective, introducing a new paradigm that leverages auxiliary information from anonymized customer engagement sessions to assist the main task of query-item link prediction. This auxiliary information is available at web scale in the form of search logs. We treat all items appearing in the same customer session as a single hyperedge. The hypothesis is that items in a customer session are unified by a common shopping interest. With these hyperedges, we augment the original bipartite graph into a new hypergraph. We develop a Dual-Channel Attention-Based Hypergraph Neural Network (DCAH), which synergizes information from two potentially noisy sources (original query-item edges and item-item hyperedges). In this way, items on the tail are better connected due to the extra hyperedges, thereby enhancing their link prediction performance. We further integrate DCAH with self-supervised graph pre-training and/or DropEdge training, both of which effectively alleviate disassortative mixing. Extensive experiments on three proprietary E-Commerce datasets show that DCAH yields significant improvements of up to 24.6% in mean reciprocal rank (MRR) and 48.3% in recall compared to GNN-based baselines. Our source code is available at https://github.com/amazon-science/dual-channel-hypergraph-neural-network.
Modality-balanced embedding for video retrieval
Authors: Xun Wang, Bingqing Ke, Xuanping Li, Fangyu Liu, Mingyu Zhang, Xiao Liang, Qiushi Xiao
Venue: Conference SIGIR 2022
Year: 2022

Video search has become the main routine for users to discover videos relevant to a text query on large short-video sharing platforms. During training a query-video bi-encoder model using online search logs,\textit we identify a modality bias phenomenon that the video encoder almost entirely relies on text matching, neglecting other modalities of the videos such as vision, audio, \etc This modality imbalance results from a) modality gap: the relevance between a query and a video text is much easier to learn as the query is also a piece of text, with the same modality as the video text; b) data bias: most training samples can be solved solely by text matching. Here we share our practices to improve the first retrieval stage including our solution for the modality imbalance issue. We propose \modelname (short for Modality Balanced Video Retrieval) with two key components: manually generated modality-shuffled (MS) samples and a dynamic margin (DM) based on visual relevance. They can encourage the video encoder to pay balanced attentions to each modality. Through extensive experiments on a real world dataset, we show empirically that our method is both effective and efficient in solving modality bias problem. We have also deployed our ~\modelname~ in a large video platform and observed statistically significant boost over a highly optimized baseline in an A/B test and manual GSB evaluations.
Essa: Explanation iterative supervision via saliency-guided data augmentation
Authors: Siyi Gu, Yifei Zhang 0006, Yuyang Gao, Xiaofeng Yang 0005, Liang Zhao 0002
Venue: Conference KDD 2023
Year: 2023

Explanation supervision is a technique in which the model is guided by human-generated explanations during training. This technique aims to improve both the interpretability and predictability of the model by incorporating human understanding into the training process. Since explanation supervision requires a large scale of training data, the data augmentation technique is necessary to be applied to increase the size and diversity of the original dataset. However, data augmentation on sophisticated data like medical images is particularly challenging due to the following: 1) scarcity of data in training the learning-based data augmenter, 2) difficulty in generating realistic and sophisticated images, and 3) difficulty in ensuring the augmented data indeed boosts the performance of explanation-guided learning. To solve these challenges, we propose an Explanation Iterative Supervision via Saliency-guided Data Augmentation (ESSA) framework for conducting explanation supervision and adversarial-trained image data augmentation via a synergized iterative loop that handles the translation from annotation to sophisticated images and the generation of synthetic image-annotation pairs with an alternating training strategy. Extensive experiments on two datasets from the medical imaging domain demonstrate the effectiveness of our proposed framework in improving both the predictability and explainability of the model.
Learning non-local range markov random field for image restoration
Authors: Jian Sun 0009, Marshall F. Tappen
Venue: Conference CVPR 2011
Year: 2011

In this paper, we design a novel MRF framework which is called Non-Local Range Markov Random Field (NLR-MRF). The local spatial range of clique in traditional MRF is extended to the non-local range which is defined over the local patch and also its similar patches in a non-local window. Then the traditional local spatial filter is extended to the non-local range filter that convolves an image over the non-local ranges of pixels. In this framework, we propose a gradient-based discriminative learning method to learn the potential functions and non-local range filter bank. As the gradients of loss function with respect to model parameters are explicitly computed, efficient gradient-based optimization methods are utilized to train the proposed model. We implement this framework for image denoising and in-painting, the results show that the learned NLR-MRF model significantly outperforms the traditional MRF models and produces state-of-the-art results.
Optimal hdr reconstruction with linear digital cameras
Authors: Miguel Granados, Boris Ajdin, Michael Wand 0001, Christian Theobalt, Hans-Peter Seidel, Hendrik P. A. Lensch
Venue: Conference CVPR 2010
Year: 2010

Given a multi-exposure sequence of a scene, our aim is to recover the absolute irradiance falling onto a linear camera sensor. The established approach is to perform a weighted average of the scaled input exposures. However, there is no clear consensus on the appropriate weighting to use. We propose a weighting function that produces statistically optimal estimates under the assumption of compound-Gaussian noise. Our weighting is based on a calibrated camera model that accounts for all noise sources. This model also allows us to simultaneously estimate the irradiance and its uncertainty. We evaluate our method on simulated and real world photographs, and show that we consistently improve the signal-to-noise ratio over previous approaches. Finally, we show the effectiveness of our model for optimal exposure sequence selection and HDR image denoising.
TD3: Tucker decomposition based dataset distillation method for sequential recommendation
Authors: Jiaqing Zhang, Mingjia Yin, Hao Wang, Yawen Li, Yuyang Ye, Xingyu Lou, Junping Du, Enhong Chen
Venue: Conference WWW 2025
Year: 2025

In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on large-scale datasets, but this also results in significant training costs. Dataset distillation has emerged as a key solution, condensing large datasets to accelerate model training while preserving model performance. However, condensing discrete and sequentially correlated user-item interactions, particularly with extensive item sets, presents considerable challenges. This paper introduces \textbf{TD3}, a novel \textbf{T}ucker \textbf{D}ecomposition based \textbf{D}ataset \textbf{D}istillation method within a meta-learning framework, designed for sequential recommendation. TD3 distills a fully expressive \emph{synthetic sequence summary} from original data. To efficiently reduce computational complexity and extract refined latent patterns, Tucker decomposition decouples the summary into four factors: \emph{synthetic user latent factor}, \emph{temporal dynamics latent factor}, \emph{shared item latent factor}, and a \emph{relation core} that models their interconnections. Additionally, a surrogate objective in bi-level optimization is proposed to align feature spaces extracted from models trained on both original data and synthetic sequence summary beyond the naïve performance matching approach. In the \emph{inner-loop}, an augmentation technique allows the learner to closely fit the synthetic summary, ensuring an accurate update of it in the \emph{outer-loop}. To accelerate the optimization process and address long dependencies, RaT-BPTT is employed for bi-level optimization. Experiments and analyses on multiple public datasets have confirmed the superiority and cross-architecture generalizability of the proposed designs. Codes are released at https://github.com/USTC-StarTeam/TD3.
Continual transformers: redundancy-free attention for online inference
Authors: Lukas Hedegaard, Arian Bakhtiarnia, Alexandros Iosifidis
Venue: Conference ICLR 2023
Year: 2023

Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token sequences. In this work, we propose novel formulations of the Scaled Dot-Product Attention, which enable Transformers to perform efficient online token-by-token inference on a continual input stream. Importantly, our modifications are purely to the order of computations, while the outputs and learned weights are identical to those of the original Transformer Encoder. We validate our Continual Transformer Encoder with experiments on the THUMOS14, TVSeries and GTZAN datasets with remarkable results: Our Continual one- and two-block architectures reduce the floating point operations per prediction by up to 63x and 2.6x, respectively, while retaining predictive performance.
Hedgeagents: A balanced-aware multi-agent financial trading system
Authors: Xiangyu Li, Yawen Zeng, Xiaofen Xing, Jin Xu, Xiangmin Xu
Venue: Conference WWW 2025
Year: 2025

As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations, impeding their practical application. Hence, there is an imperative to explore a more robust and resilient framework. This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ''hedging'' strategies. In this well-balanced system, an array of hedging agents has been tailored, where HedgeAgents consist of a central fund manager and multiple hedging experts specializing in various financial asset classes. These agents leverage LLMs' cognitive capabilities to make decisions and coordinate through three types of conferences. Benefiting from the powerful understanding of LLMs, our HedgeAgents attained a 70% annualized return and a 400% total return over a period of 3 years. Moreover, we have observed with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/).
Moving in stereo: Efficient structure and motion using lines
Authors: Manmohan Krishna Chandraker, Jongwoo Lim, David J. Kriegman
Venue: Conference ICCV 2009
Year: 2009

We present a fast and robust system for estimating structure and motion using a stereo pair, with straight lines as features. Our first set of contributions are efficient algorithms to perform this estimation using a few (two or three) lines, which are well-suited for use in a hypothesize-and-test framework. Our second contribution is the design of an efficient structure from motion system that performs robustly in complex indoor environments. By using infinite lines rather than line segments, our approach avoids the issues arising due to uncertain determination of end-points. Our cost function stems from a rank condition on planes backprojected from corresponding image lines. We propose a framework that imposes orthonormality constraints on the rigid body motion and can perform estimation using only two or three lines, through efficient solution of an overdetermined system of polynomials. This is in contrast to simple approaches which first reconstruct 3D lines and then align them, but perform poorly in real-world scenes with narrow baseline stereo. Experiments using synthetic as well as real data demonstrate the speed, accuracy and reliability of our system.
Prompt optimizer of text-to-image diffusion models for abstract concept understanding
Authors: Zezhong Fan, Xiaohan Li, Kaushiki Nag, Chenhao Fang, Topojoy Biswas, Jianpeng Xu, Kannan Achan
Venue: Conference WWW 2024
Year: 2024

The rapid evolution of text-to-image diffusion models has opened the door of generative AI, enabling the translation of textual descriptions into visually compelling images with remarkable quality. However, a persistent challenge within this domain is the optimization of prompts to effectively convey abstract concepts into concrete objects. For example, text encoders can hardly express "peace", while can easily illustrate olive branches and white doves. This paper introduces a novel approach named Prompt Optimizer for Abstract Concepts (POAC) specifically designed to enhance the performance of text-to-image diffusion models in interpreting and generating images from abstract concepts. We propose a Prompt Language Model (PLM), which is initialized from a pre-trained language model, and then fine-tuned with a curated dataset of abstract concept prompts. The dataset is created with GPT-4 to extend the abstract concept to a scene and concrete objects. Our framework employs a Reinforcement Learning (RL)-based optimization strategy, focusing on the alignment between the generated images by a stable diffusion model and optimized prompts. Through extensive experiments, we demonstrate that our proposed POAC significantly improves the accuracy and aesthetic quality of generated images, particularly in the description of abstract concepts and alignment with optimized prompts. We also present a comprehensive analysis of our model's performance across diffusion models under different settings, showcasing its versatility and effectiveness in enhancing abstract concept representation.
Image webs: Computing and exploiting connectivity in image collections
Authors: Kyle Heath, Natasha Gelfand, Maks Ovsjanikov, Mridul Aanjaneya, Leonidas J. Guibas
Venue: Conference CVPR 2010
Year: 2010

The widespread availability of digital cameras and ubiquitous Internet access have facilitated the creation of massive image collections. These collections can be highly interconnected through implicit links between image pairs viewing the same or similar objects. We propose building graphs called Image Webs to represent such connections. While earlier efforts studied local neighborhoods of such graphs, we are interested in understanding global structure and exploiting connectivity at larger scales. We show how to efficiently construct Image Webs that capture the connectivity in an image collection using spectral graph theory. Our technique can link together tens of thousands of images in a few minutes using a computer cluster. We also demonstrate applications for exploring collections based on global topological analysis.
DiFacto: Distributed Factorization Machines
Authors: Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang
Venue: Conference WSDM 2016
Year: 2016

Factorization Machines offer good performance and useful embeddings of data. However, they are costly to scale to large amounts of data and large numbers of features. In this paper we describe DiFacto, which uses a refined Factorization Machine model with sparse memory adaptive constraints and frequency adaptive regularization. We show how to distribute DiFacto over multiple machines using the Parameter Server framework by computing distributed subgradients on minibatches asynchronously. We analyze its convergence and demonstrate its efficiency in computational advertising datasets with billions examples and features.
Deep learning as a mixed convex-combinatorial optimization problem
Authors: Abram L. Friesen, Pedro M. Domingos
Venue: Conference ICLR 2018
Year: 2018

As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable components and must avoid vanishing and exploding gradients for effective learning. However, since gradient descent is not applicable to hard-threshold functions, it is not clear how to learn networks of them in a principled way. We address this problem by observing that setting targets for hard-threshold hidden units in order to minimize loss is a discrete optimization problem, and can be solved as such. The discrete optimization goal is to find a set of targets such that each unit, including the output, has a linearly separable problem to solve. Given these targets, the network decomposes into individual perceptrons, which can then be learned with standard convex approaches. Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case. Empirically, we show that our algorithm improves classification accuracy in a number of settings, including for AlexNet and ResNet-18 on ImageNet, when compared to the straight-through estimator.