pwc-archive/papers-with-abstracts huggingface dataset:
CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

In drug discovery, it is vital to confirm the predictions of pharmaceutical properties from computational models using costly wet-lab experiments. Hence, obtaining reliable uncertainty estimates is crucial for prioritizing drug molecules for subsequent experimental validation. Conformal Prediction (CP) is a promising tool for creating such prediction sets for molecular properties with a coverage guarantee. However, the exchangeability assumption of CP is often challenged with covariate shift in drug discovery tasks: Most datasets contain limited labeled data, which may not be representative of the vast chemical space from which molecules are drawn. To address this limitation, we propose a method called CoDrug that employs an energy-based model leveraging both training data and unlabelled data, and  Kernel Density Estimation (KDE) to assess the densities of a molecule set. The estimated densities are then used to weigh the molecule samples while building prediction sets and rectifying for distribution shift. In extensive experiments involving realistic distribution drifts in various small-molecule drug discovery tasks,  we demonstrate the ability of CoDrug to provide valid prediction sets and its utility in addressing the distribution shift arising from de novo drug design models. On average, using CoDrug can reduce the coverage gap by over 35% when compared to conformal prediction sets not adjusted for covariate shift.
An Efficient Dataset Condensation Plugin and Its Application to Continual Learning
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Dataset condensation (DC) distills a large real-world dataset into a small synthetic dataset, with the goal of training a network from scratch on the latter that performs similarly to the former. State-of-the-art (SOTA) DC methods have achieved satisfactory results through techniques such as accuracy, gradient, training trajectory, or distribution matching. However, these works all perform matching in the high-dimension pixel spaces, ignoring that natural images are usually locally connected and have lower intrinsic dimensions, resulting in low condensation efficiency.  In this work, we propose a simple-yet-efficient dataset condensation plugin that matches the raw and synthetic datasets in a low-dimensional manifold. Specifically, our plugin condenses raw images into two low-rank matrices instead of parameterized image matrices. Our plugin can be easily incorporated into existing DC methods, thereby containing richer raw dataset information at limited storage costs to improve the downstream applications' performance.  We verify on multiple public datasets that when the proposed plugin is combined with SOTA DC methods, the performance of the network trained on synthetic data is significantly improved compared to traditional DC methods. Moreover, when applying the DC methods as a plugin to continual learning tasks, we observed that our approach effectively mitigates catastrophic forgetting of old tasks under limited memory buffer constraints and avoids the problem of raw data privacy leakage.Submission Number: 5810
DAW: Exploring the Better Weighting Function for Semi-supervised Semantic Segmentation
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

The critical challenge of semi-supervised semantic segmentation lies in how to fully exploit a large volume of unlabeled data to improve the model’s generalization performance for robust segmentation.  Existing methods tend to employ certain criteria (weighting function) to select pixel-level pseudo labels. However, the trade-off exists between inaccurate yet utilized pseudo-labels, and correct yet discarded pseudo-labels in these methods when handling pseudo-labels without thoughtful consideration of the weighting function, hindering the generalization ability of the model. In this paper, we systematically analyze the trade-off in previous methods when dealing with pseudo-labels. We formally define the trade-off between inaccurate yet utilized pseudo-labels, and correct yet discarded pseudo-labels by explicitly modeling the confidence distribution of correct and inaccurate pseudo-labels, equipped with a unified weighting function. To this end, we propose Distribution-Aware Weighting (DAW) to strive to minimize the negative equivalence impact raised by the trade-off. We find an interesting fact that the optimal solution for the weighting function is a hard step function, with the jump point located at the intersection of the two confidence distributions. Besides, we devise distribution alignment to mitigate the issue of the discrepancy between the prediction distributions of labeled and unlabeled data. Extensive experimental results on multiple benchmarks including mitochondria segmentation demonstrate that DAW performs favorably against state-of-the-art methods.
How2comm: Communication-Efficient and Collaboration-Pragmatic Multi-Agent Perception
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Multi-agent collaborative perception has recently received widespread attention as an emerging application in driving scenarios. Despite the advancements in previous efforts, challenges remain due to various noises in the perception procedure, including communication redundancy, transmission delay, and collaboration heterogeneity. To tackle these issues, we propose \textit{How2comm}, a collaborative perception framework that seeks a trade-off between perception performance and communication bandwidth. Our novelties lie in three aspects. First, we devise a mutual information-aware communication mechanism to maximally sustain the informative features shared by collaborators. The spatial-channel filtering is adopted to perform effective feature sparsification for efficient communication. Second, we present a flow-guided delay compensation strategy to predict future characteristics from collaborators and eliminate feature misalignment due to temporal asynchrony. Ultimately, a pragmatic collaboration transformer is introduced to integrate holistic spatial semantics and temporal context clues among agents. Our framework is thoroughly evaluated on several LiDAR-based collaborative detection datasets in real-world and simulated scenarios. Comprehensive experiments demonstrate the superiority of How2comm and the effectiveness of all its vital components. The code will be released at https://github.com/ydk122024/How2comm.
Deep Non-line-of-sight Imaging from Under-scanning Measurements
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Active confocal non-line-of-sight (NLOS) imaging has successfully enabled seeing around corners relying on high-quality transient measurements. However, acquiring spatial-dense transient measurement is time-consuming, raising the question of how to reconstruct satisfactory results from under-scanning measurements (USM). The existing solutions, involving the traditional algorithms, however, are hindered by unsatisfactory results or long computing times. To this end, we propose the first deep-learning-based approach to NLOS imaging from USM. Our proposed end-to-end network is composed of two main components: the transient recovery network (TRN) and the volume reconstruction network (VRN). Specifically, TRN takes the under-scanning measurements as input, utilizes a multiple kernel feature extraction module and a multiple feature fusion module, and outputs sufficient-scanning measurements at the high-spatial resolution. Afterwards, VRN incorporates the linear physics prior of the light-path transport model and reconstructs the hidden volume representation. Besides, we introduce regularized constraints that enhance the perception of more local details while suppressing smoothing effects. The proposed method achieves superior performance on both synthetic data and public real-world data, as demonstrated by extensive experimental results with different under-scanning grids. Moreover, the proposed method delivers impressive robustness at an extremely low scanning grid (i.e., 8$\times$8) and offers high-speed inference (i.e., 50 times faster than the existing iterative solution).
KD-Zero: Evolving Knowledge Distiller for Any Teacher-Student Pairs
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Knowledge distillation (KD) has emerged as an effective technique for compressing models that can enhance the lightweight model.  Conventional KD methods propose various designs to allow student model to imitate the teacher better.  However, these handcrafted KD designs heavily rely on expert knowledge and may be sub-optimal for various teacher-student pairs.  In this paper, we present a novel framework, KD-Zero, which utilizes evolutionary search to automatically discover promising distiller  from scratch for any teacher-student architectures.  Specifically, we first decompose the generalized distiller into knowledge transformations, distance functions, and loss weights.  Then,  we construct our distiller search space by selecting advanced operations for these three components.  With sharpness and represent gap as fitting objectives, we evolve candidate populations and generate better distillers by crossover and mutation.  To ensure efficient searching, we employ the loss-rejection protocol, search space shrinkage, and proxy settings during the search process.  In this manner, the discovered distiller can address the capacity gap and cross-architecture challenges for any teacher-student pairs in the final distillation stage.  Comprehensive experiments reveal that KD-Zero consistently outperforms other state-of-the-art methods across diverse architectures on classification, detection, and segmentation tasks.  Noticeably, we provide some practical insights in designing the distiller by analyzing the distiller discovered.  Code is at https://github.com/lilujunai/KD-Zero.
SLIBO-Net: Floorplan Reconstruction via Slicing Box Representation with Local Geometry Regularization
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

This paper focuses on improving the reconstruction of 2D floorplans from unstructured 3D point clouds. We identify opportunities for enhancement over the existing methods in three main areas: semantic quality, efficient representation, and local geometric details. To address these, we presents SLIBO-Net, an innovative approach to reconstructing 2D floorplans from unstructured 3D point clouds. We propose a novel transformer-based architecture that employs an efficient floorplan representation, providing improved room shape supervision and allowing for manageable token numbers. By incorporating geometric priors as a regularization mechanism and post-processing step, we enhance the capture of local geometric details. We also propose a scale-independent evaluation metric, correcting the discrepancy in error treatment between varying floorplan sizes. Our approach notably achieves a new state-of-the-art on the Structure3D dataset. The resultant floorplans exhibit enhanced semantic plausibility, substantially improving the overall quality and realism of the reconstructions. Our code and dataset are available online.
Transition-constant Normalization for Image Enhancement
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Normalization techniques that capture image style by statistical representation have become a popular component in deep neural networks.
Although image enhancement can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance. 
To fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks.
Specifically, it consists of two streams of normalization operations arranged under an invertible constraint, along with a feature sub-sampling operation that satisfies the normalization constraint.
TCN enjoys several merits, including being parameter-free, plug-and-play, and incurring no additional computational costs.
We provide various formats to utilize TCN for image enhancement, including seamless  integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures.
Through extensive experiments on multiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements.
Besides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation.
The code is available at  \textit{\textcolor{blue}{https://github.com/huangkevinj/TCNorm}}.
Synthetic-to-Real Pose Estimation with Geometric Reconstruction
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Pose estimation is remarkably successful under supervised learning, but obtaining annotations, especially for new deployments, is costly and time-consuming. This work tackles adapting models trained on synthetic data to real-world target domains with only unlabelled data. A common approach is model fine-tuning with pseudo-labels from the target domain; yet many pseudo-labelling strategies cannot provide sufficient high-quality pose labels. This work proposes a reconstruction-based strategy as a complement to pseudo-labelling for synthetic-to-real domain adaptation. We generate the driving image by geometrically transforming a base image according to the predicted keypoints and enforce a reconstruction loss to refine the predictions. It provides a novel solution to effectively correct confident yet inaccurate keypoint locations through image reconstruction in domain adaptation. Our approach outperforms the previous state-of-the-arts by 8% for PCK on four large-scale hand and human real-world datasets. In particular, we excel on endpoints such as fingertips and head, with 7.2% and 29.9% improvements in PCK.
PromptIR: Prompting for All-in-One Image Restoration
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Image restoration involves recovering a high-quality clean image from its degraded version. Deep learning-based methods have significantly improved image restoration performance, however, they have limited generalization ability to different degradation types and levels. This restricts their real-world application since it requires training individual models for each specific degradation and knowing the input degradation type to apply the relevant model. We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation. In particular, our method uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network. This allows our method to generalize to different degradation types and levels, while still achieving state-of-the-art results on image denoising, deraining, and dehazing.  Overall, PromptIR offers a generic and efficient plugin module with few lightweight prompts that can be used to restore images of various types and levels of degradation with no prior information on the corruptions present in the image. Our code and pre-trained models are available here: https://github.com/va1shn9v/PromptIR
Unified 3D Segmenter As Prototypical Classifiers
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

The task of point cloud segmentation, comprising semantic, instance, and panoptic segmentation, has been mainly tackled by designing task-specific network architectures, which often lack the flexibility to generalize across tasks, thus resulting in a fragmented research landscape. In this paper, we introduce ProtoSEG, a prototype-based model that unifies semantic, instance, and panoptic segmentation tasks. Our approach treats these three homogeneous tasks as a classification problem with different levels of granularity. By leveraging a Transformer architecture, we extract point embeddings to optimize prototype-class distances and dynamically learn class prototypes to accommodate the end tasks. Our prototypical design enjoys simplicity and transparency, powerful representational learning, and ad-hoc explainability. Empirical results demonstrate that ProtoSEG outperforms concurrent well-known specialized architectures on 3D point cloud benchmarks, achieving 72.3%, 76.4% and 74.2% mIoU for semantic segmentation on S3DIS, ScanNet V2 and SemanticKITTI, 66.8% mCov and 51.2% mAP for instance segmentation on S3DIS and ScanNet V2, 62.4% PQ for panoptic segmentation on SemanticKITTI, validating the strength of our concept and the effectiveness of our algorithm.  The code and models are available at https://github.com/zyqin19/PROTOSEG.
Exploiting Contextual Objects and Relations for 3D Visual Grounding
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

3D visual grounding, the task of identifying visual objects in 3D scenes based on natural language inputs, plays a critical role in enabling machines to understand and engage with the real-world environment. However, this task is challenging due to the necessity to capture 3D contextual information to distinguish target objects from complex 3D scenes. The absence of annotations for contextual objects and relations further exacerbates the difficulties. In this paper, we propose a novel model, CORE-3DVG, to address these challenges by explicitly learning about contextual objects and relations. Our method accomplishes 3D visual grounding via three sequential modular networks, including a text-guided object detection network, a relation matching network, and a target identification network. During training, we introduce a pseudo-label self-generation strategy and a weakly-supervised method to facilitate the learning of contextual objects and relations, respectively. The proposed techniques allow the networks to focus more effectively on referred objects within 3D scenes by understanding their context better. We validate our model on the challenging Nr3D, Sr3D, and ScanRefer datasets and demonstrate state-of-the-art performance. Our code will be public at https://github.com/yangli18/CORE-3DVG.
PaintSeg: Painting Pixels for Training-free Segmentation
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without any training. We propose an adversarial masked contrastive painting (AMCP) process, which creates a contrast between the original image and a painted image in which a masked area is painted using off-the-shelf generative models. During the painting process, inpainting and outpainting are alternated, with the former masking the foreground and filling in the background, and the latter masking the background while recovering the missing part of the foreground object. Inpainting and outpainting, also referred to as I-step and O-step, allow our method to gradually advance the target segmentation mask toward the ground truth without supervision or training. PaintSeg can be configured to work with a variety of prompts, e.g. coarse masks, boxes, scribbles, and points. Our experimental results demonstrate that PaintSeg outperforms existing approaches in coarse mask-prompt, box-prompt, and point-prompt segmentation tasks, providing a training-free solution suitable for unsupervised segmentation. Code: https://github.com/lxa9867/PaintSeg.
DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

We propose a novel neural network-based approach to modeling protein dynamics using an implicit representation of a protein’s surface in 3D and time. Our method utilizes the zero-level set of signed distance functions (SDFs) to represent protein surfaces, enabling temporally and spatially continuous representations of protein dynamics. Our experimental results demonstrate that our model accurately captures protein dynamic trajectories and can interpolate and extrapolate in 3D and time. Importantly, this is the first study to introduce this method and successfully model large-scale protein dynamics. This approach offers a promising alternative to current methods, overcoming the limitations of first-principles-based and deep learning methods, and provides a more scalable and efficient approach to modeling protein dynamics. Additionally, our surface representation approach simplifies calculations and allows identifying movement trends and amplitudes of protein domains, making it a useful tool for protein dynamics research. Codes are available at https://github.com/Sundw-818/DSR, and we have a project webpage that shows some video results, https://sundw-818.github.io/DSR/.
An Iterative Self-Learning Framework for Medical Domain Generalization
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Deep learning models have been widely used to assist doctors with clinical decision-making. However, these models often encounter a significant performance drop when applied to data that differs from the distribution they were trained on. This challenge is known as the domain shift problem. Existing domain generalization algorithms attempt to address this problem by assuming the availability of domain IDs and training a single model to handle all domains. However, in healthcare settings, patients can be classified into numerous latent domains, where the actual domain categorizations are unknown. Furthermore, each patient domain exhibits distinct clinical characteristics, making it sub-optimal to train a single model for all domains. To overcome these limitations, we propose SLGD, a self-learning framework that iteratively discovers decoupled domains and trains personalized classifiers for each decoupled domain. We evaluate the generalizability of SLGD across spatial and temporal data distribution shifts on two real-world public EHR datasets: eICU and MIMIC-IV. Our results show that SLGD achieves up to 11% improvement in the AUPRC score over the best baseline.
REFINE: A Fine-Grained Medication Recommendation System Using Deep Learning and Personalized Drug Interaction Modeling
Date: 2023-09-21 00:00:00
Proceeding: NeurIPS 2023 11

Patients with co-morbidities often require multiple medications to manage their conditions. However, existing medication recommendation systems only offer class-level medications and regard all interactions among drugs to have the same level of severity. This limits their ability to provide personalized and safe recommendations tailored to individual needs. In this work, we introduce a deep learning-based fine-grained medication recommendation system called REFINE, which is designed to improve treatment outcomes and minimize adverse drug interactions. In order to better characterize patients’ health conditions, we model the trend in medication dosage titrations and lab test responses, and adapt the vision transformer to obtain effective patient representations. We also model drug interaction severity levels as weighted graphs to learn safe drug combinations and design a balanced loss function to avoid overly conservative recommendations and miss medications that might be needed for certain conditions. Extensive experiments on two real-world datasets show that REFINE outperforms state-of-the-art techniques.