CVPR 2024 Papers

We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplored yet highly practical problem of SSDG we make the following core contributions. First we propose a feature-based conformity technique that matches the posterior distributions from the feature space with the pseudo-label from the model's output space. Second we develop a semantics alignment loss to learn semantically-compatible representations by regularizing the semantic structure in the feature space. Our method is plug-and-play and can be readily integrated with different SSL-based SSDG baselines without introducing any additional parameters. Extensive experimental results across five challenging DG benchmarks with four strong SSL baselines suggest that our method provides consistent and notable gains in two different SSDG settings.

Tumor synthesis enables the creation of artificial tumors in medical images facilitating the training of AI models for tumor detection and segmentation. However success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and furthermore the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g. hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT) whether they originate in the liver pancreas or kidneys. We have ascertained that generative AI models e.g. Diffusion Models can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes encompassing a broad spectrum of patient demographics imaging protocols and healthcare facilities.

Multi-Object Tracking (MOT) encompasses various tracking scenarios each characterized by unique traits. Effective trackers should demonstrate a high degree of generalizability across diverse scenarios. However existing trackers struggle to accommodate all aspects or necessitate hypothesis and experimentation to customize the association information (motion and/or appearance) for a given scenario leading to narrowly tailored solutions with limited generalizability. In this paper we investigate the factors that influence trackers' generalization to different scenarios and concretize them into a set of tracking scenario attributes to guide the design of more generalizable trackers. Furthermore we propose a "point-wise to instance-wise relation" framework for MOT i.e. GeneralTrack which can generalize across diverse scenarios while eliminating the need to balance motion and appearance. Thanks to its superior generalizability our proposed GeneralTrack achieves state-of-the-art performance on multiple benchmarks and demonstrates the potential for domain generalization.

The robustness of convolutional neural networks (CNNs) is vital to modern AI-driven systems. It can be quantified by formal verification by providing a certified lower bound within which any perturbation does not alter the original input's classification result. It is challenging due to nonlinear components such as MaxPool. At present many verification methods are sound but risk losing some precision to enhance efficiency and scalability and thus a certified lower bound is a crucial criterion for evaluating the performance of verification tools. In this paper we present MaxLin a robustness verifier for MaxPool-based CNNs with tight Linear approximation. By tightening the linear approximation of the MaxPool function we can certify larger certified lower bounds of CNNs. We evaluate MaxLin with open-sourced benchmarks including LeNet and networks trained on the MNIST CIFAR-10 and Tiny ImageNet datasets. The results show that MaxLin outperforms state-of-the-art tools with up to 110.60% improvement regarding the certified lower bound and 5.13 X speedup for the same neural networks. Our code is available at https://github.com/xiaoyuanpigo/maxlin.

Although adversarial training (AT) has proven effective in enhancing the model's robustness the recently revealed issue of fairness in robustness has not been well addressed i.e. the robust accuracy varies significantly among different categories. In this paper instead of uniformly evaluating the model's average class performance we delve into the issue of robust fairness by considering the worst-case distribution across various classes. We propose a novel learning paradigm named Fairness-Aware Adversarial Learning (FAAL). As a generalization of conventional AT we re-define the problem of adversarial training as a min-max-max framework to ensure both robustness and fairness of the trained model. Specifically by taking advantage of distributional robust optimization our method aims to find the worst distribution among different categories and the solution is guaranteed to obtain the upper bound performance with high probability. In particular FAAL can fine-tune an unfair robust model to be fair within only two epochs without compromising the overall clean and robust accuracies. Extensive experiments on various image datasets validate the superior performance and efficiency of the proposed FAAL compared to other state-of-the-art methods.

In Federated Learning (FL) the data in each client is typically assumed fixed or static. However data often comes in an incremental manner in real-world applications where the data domain may increase dynamically. In this work we study catastrophic forgetting with data heterogeneity in Federated Incremental Learning (FIL) scenarios where edge clients may lack enough storage space to retain full data. We propose to employ a simple generic framework for FIL named Re-Fed which can coordinate each client to cache important samples for replay. More specifically when a new task arrives each client first caches selected previous samples based on their global and local importance. Then the client trains the local model with both the cached samples and the samples from the new task. Theoretically we analyze the ability of Re-Fed to discover important samples for replay thus alleviating the catastrophic forgetting problem. Moreover we empirically show that Re-Fed achieves competitive performance compared to state-of-the-art methods.

Vanilla text-to-image diffusion models struggle with generating accurate human images commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs. Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls --- human-centric priors such as pose or depth maps --- during the image generation phase. This paper explores the integration of these human-centric priors directly into the model fine-tuning stage essentially eliminating the need for extra conditions at the inference stage. We realize this idea by proposing a human-centric alignment loss to strengthen human-related information from the textual prompts within the cross-attention maps. To ensure semantic detail richness and human structural accuracy during fine-tuning we introduce scale-aware and step-wise constraints within the diffusion process according to an in-depth analysis of the cross-attention layer. Extensive experiments show that our method largely improves over state-of-the-art text-to-image models to synthesize high-quality human images based on user-written prompts.

6DoF Pose estimation has been gaining increased importance in vision for over a decade however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras HDR and algorithms targeted at reliable high-accuracy industrial automation. Specifically we capture 2300 physical scenes of 20 industrial parts covering a 1mx1mx0.5m working volume resulting in over 100000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100000 lux. We also present validate and analyze robot consistency an evaluation method targeted at scalable high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.

The problem of calibrating deep neural networks (DNNs) for multi-label learning is considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot classification are poorly calibrated. Many calibration techniques have been proposed to address the problem. However little attention has been paid to the calibration of multi-label DNNs. In this literature the focus has been on improving labeling accuracy in the face of severe dataset unbalance. This is addressed by the introduction of asymmetric losses which have became very popular. However these losses do not induce well calibrated classifiers. In this work we first provide a theoretical explanation for this poor calibration performance by showing that these loses losses lack the strictly proper property a necessary condition for accurate probability estimation. To overcome this problem we propose a new Strictly Proper Asymmetric (SPA) loss. This is complemented by a Label Pair Regularizer (LPR) that increases the number of calibration constraints introduced per training example. The effectiveness of both contributions is validated by extensive experiments on various multi-label datasets. The resulting training method is shown to significantly decrease the calibration error while maintaining state-of-the-art accuracy.

3D visual grounding plays a crucial role in scene understanding with extensive applications in AR/VR. Despite the significant progress made in recent methods the requirement of dense textual descriptions for each individual object which is time-consuming and costly hinders their scalability. To mitigate reliance on text annotations during training researchers have explored language-free training paradigms in the 2D field via explicit text generation or implicit feature substitution. Nevertheless unlike 2D images the complexity of spatial relations in 3D coupled with the absence of robust 3D visual language pre-trained models makes it challenging to directly transfer previous strategies. To tackle the above issues in this paper we introduce a language-free training framework for 3D visual grounding. By utilizing the visual-language joint embedding in 2D large cross-modality model as a bridge we can expediently produce the pseudo-language features by leveraging the features of 2D images which are equivalent to that of real textual descriptions. We further develop a relation injection scheme with a Neighboring Relation-aware Modeling module and a Cross-modality Relation Consistency module aiming to enhance and preserve the complex relationships between the 2D and 3D embedding space. Extensive experiments demonstrate that our proposed language-free 3D visual grounding approach can obtain promising performance across three widely used datasets --ScanRefer Nr3D and Sr3D. Our codes are available at https://github.com/xibi777/3DLFVG

Vision-language (VL) models have achieved unprecedented success recently in which the connection module is the key to bridge the modality gap. Nevertheless the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side most existing approaches only use the last feature of the vision tower without using the low-level features. On the language side most existing methods only introduce shallow vision-language interactions. In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models. To take advantage of the lowerlevel information from the vision tower a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of-the-art method by 18.1 CIDEr when training from scratch on the COCO caption task which greatly improves the data efficiency. When used as a plug-in module VIVL consistently improves the performance for various backbones and VL frameworks delivering new state-of-the-art results on multiple benchmarks e.g. NoCaps and VQAv2.

This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g. adapting to new data or target bitrates) without breaking backward compatibility the ability to decode bitstreams encoded by the original model. We refer to this problem as continual learning of image compression. Our initial findings show that baseline solutions such as end-to-end fine-tuning do not preserve the desired backward compatibility. To tackle this we propose a knowledge replay training strategy that effectively addresses this issue. We also design a new model architecture that enables more effective continual learning than existing baselines. Experiments are conducted for two scenarios: data-incremental learning and rate-incremental learning. The main conclusion of this paper is that neural image compressors can be fine-tuned to achieve better performance (compared to their pre-trained version) on new data and rates without compromising backward compatibility. The code is publicly available online.

Movie trailers are an essential tool for promoting films and attracting audiences. However the process of creating trailers can be time-consuming and expensive. To streamline this process we propose an automatic trailer generation framework that generates plausible trailers from a full movie by automating shot selection and composition. Our approach draws inspiration from machine translation techniques and models the movies and trailers as sequences of shots thus formulating the trailer generation problem as a sequence-to-sequence task. We introduce Trailer Generation Transformer (TGT) a deep-learning framework utilizing an encoder-decoder architecture. TGT movie encoder is tasked with contextualizing each movie shot representation via self-attention while the autoregressive trailer decoder predicts the feature representation of the next trailer shot accounting for the relevance of shots' temporal order in trailers. Our TGT significantly outperforms previous methods on a comprehensive suite of metrics.

To defend deep neural networks from adversarial attacks adversarial training has been drawing increasing attention for its effectiveness. However the accuracy and robustness resulting from the adversarial training are limited by the architecture because adversarial training improves accuracy and robustness by adjusting the weight connection affiliated to the architecture. In this work we propose ARNAS to search for accurate and robust architectures for adversarial training. First we design an accurate and robust search space in which the placement of the cells and the proportional relationship of the filter numbers are carefully determined. With the design the architectures can obtain both accuracy and robustness by deploying accurate and robust structures to their sensitive positions respectively. Then we propose a differentiable multi-objective search strategy performing gradient descent towards directions that are beneficial for both natural loss and adversarial loss thus the accuracy and robustness can be guaranteed at the same time. We conduct comprehensive experiments in terms of white-box attacks black-box attacks and transferability. Experimental results show that the searched architecture has the strongest robustness with the competitive accuracy and breaks the traditional idea that NAS-based architectures cannot transfer well to complex tasks in robustness scenarios. By analyzing outstanding architectures searched we also conclude that accurate and robust neural architectures tend to deploy different structures near the input and output which has great practical significance on both hand-crafting and automatically designing of accurate and robust architectures.

In this paper we propose an accurate post-training quantization framework of diffusion models (APQ-DM) for efficient image generation. Conventional quantization frameworks learn shared quantization functions for tensor discretization regardless of the generation timesteps in diffusion models while the activation distribution differs significantly across various timesteps. Meanwhile the calibration images are acquired in random timesteps which fail to provide sufficient information for generalizable quantization function learning. Both issues cause sizable quantization errors with obvious image generation performance degradation. On the contrary we design distribution-aware quantization functions for activation discretization in different timesteps and search the optimal timesteps for informative calibration image generation so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead. Specifically we partition various timestep quantization functions into different groups according to the importance weights which are optimized by differentiable search algorithms. We also extend structural risk minimization principle for informative calibration image generation to enhance the generalization ability in the deployment of quantized diffusion model. Extensive experimental results show that our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost.

We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature energy-efficient low-cost single-photon cameras. These cameras operating as time resolved image sensors illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse as it returns back from the scene at a high temporal resolution. We propose to model this image formation process account for its non-idealities and adapt neural rendering to reconstruct 3D geometry from a set of spatially distributed sensors with known poses. We show that our approach can successfully recover complex 3D shapes from simulated data. We further demonstrate 3D object reconstruction from real-world captures utilizing measurements from a commodity proximity sensor. Our work draws a connection between image-based modeling and active range scanning and offers a step towards 3D vision with single-photon cameras.

This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have shown that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets but their methods rely heavily on handcrafted text prompts about defects making them difficult to generalize to anomalies in other applications e.g. medical image anomalies or semantic anomalies in natural images. In this work we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly. To this end we introduce a novel approach that learns an in-context residual learning model for GAD termed InCTRL. It is trained on an auxiliary dataset to discriminate anomalies from normal samples based on a holistic evaluation of the residuals between query images and few-shot normal sample prompts. Regardless of the datasets per definition of anomaly larger residuals are expected for anomalies than normal samples thereby enabling InCTRL to generalize across different domains without further training. Comprehensive experiments on nine AD datasets are performed to establish a GAD benchmark that encapsulate the detection of industrial defect anomalies medical anomalies and semantic anomalies in both one-vs-all and multi-class setting on which InCTRL is the best performer and significantly outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/InCTRL.

Scene reconstruction from multi-view images is a fundamental problem in computer vision and graphics. Recent neural implicit surface reconstruction methods have achieved high-quality results; however editing and manipulating the 3D geometry of reconstructed scenes remains challenging due to the absence of naturally decomposed object entities and complex object/background compositions. In this paper we present Total-Decom a novel method for decomposed 3D reconstruction with minimal human interaction. Our approach seamlessly integrates the Segment Anything Model (SAM) with hybrid implicit-explicit neural surface representations and a mesh-based region-growing technique for accurate 3D object decomposition. Total-Decom requires minimal human annotations while providing users with real-time control over the granularity and quality of decomposition. We extensively evaluate our method on benchmark datasets and demonstrate its potential for downstream applications such as animation and scene editing.