CVPR 2024 Papers

Largely due to their implicit nature neural fields lack a direct mechanism for filtering as Fourier analysis from discrete signal processing is not directly applicable to these representations. Effective filtering of neural fields is critical to enable level-of-detail processing in downstream applications and support operations that involve sampling the field on regular grids (e.g. marching cubes). Existing methods that attempt to decompose neural fields in the frequency domain either resort to heuristics or require extensive modifications to the neural field architecture. We show that via a simple modification one can obtain neural fields that are low-pass filtered and in turn show how this can be exploited to obtain a frequency decomposition of the entire signal. We demonstrate the validity of our technique by investigating level-of-detail reconstruction and showing how coarser representations can be computed effectively.

In this paper we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM known for its zero-shot generalizability exhibits a performance degradation when faced with datasets with varying image sizes. Previous approaches tend to resize the image to a fixed size or adopt structure modifications hindering the preservation of SAM's rich prior knowledge. Besides such task-specific tuning necessitates a complete retraining of the model which is cost-expensive and unacceptable for deployment in the downstream tasks. In this paper we reformulate this challenge as a length extrapolation problem where token sequence length varies while maintaining a consistent patch size for images with different sizes. To this end we propose a Scalable Bias-Mode Attention Mask (BA-SAM) to enhance SAM's adaptability to varying image resolutions while eliminating the need for structure modifications. Firstly we introduce a new scaling factor to ensure consistent magnitude in the attention layer's dot product values when the token sequence length changes. Secondly we present a bias-mode attention mask that allows each token to prioritize neighboring information mitigating the impact of untrained distant information. Our BA-SAM demonstrates efficacy in two scenarios: zero-shot and fine-tuning. Extensive evaluation of diverse datasets including DIS5K DUTS ISIC COD10K and COCO reveals its ability to significantly mitigate performance degradation in the zero-shot setting and achieve state-of-the-art performance with minimal fine-tuning. Furthermore we propose a generalized model and benchmark showcasing BA-SAM's generalizability across all four datasets simultaneously.

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently the spatio-temporal information is far away from being fully explored. To alleviate this issue we propose an adaptive tracker with spatio-temporal transformers (named AQATrack) which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally based on the initial target template and learnt autoregressive queries a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT LaSOText TrackingNet GOT-10k TNL2K and UAV123. Code and models will be https://github.com/orgs/GXNU-ZhongLab.

We present EgoTAP a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge prior methods employ joint heatmaps-probabilistic 2D representations of the body pose but heatmap-to-3D pose conversion still remains an inaccurate process. We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into effective feature embedding using self-attention. Then the Propagation Network estimates the 3D pose by utilizing skeletal information to better estimate the position of obscure joints. Our method significantly outperforms the previous state-of-the-art qualitatively and quantitatively demonstrated by a 23.9% reduction of error in an MPJPE metric. Our source code is available on GitHub.

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless conventional prompt learning methods overfit the training distribution and lose the generalization ability on the test distributions. To improve the generalization across various distribution shifts we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this framework the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts.

The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework where clients update local models based on a global model aggregated by the server from the previous training round. This process can cause client drift especially with significant cross-client data heterogeneity impacting model performance and convergence of the FL algorithm. To address these challenges we introduce FedAF a novel aggregation-free FL algorithm. In this framework clients collaboratively learn condensed data by leveraging peer knowledge the server subsequently trains the global model using the condensed data and soft labels received from the clients. FedAF inherently avoids the issue of client drift enhances the quality of condensed data amid notable data heterogeneity and improves the global model performance. Extensive numerical studies on several popular benchmark datasets show FedAF surpasses various state-of-the-art FL algorithms in handling label-skew and feature-skew data heterogeneity leading to superior global model accuracy and faster convergence.

In Re-identification (ReID) recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However the challenge persists in developing a unified framework that could effectively handle varying multimodal data including RGB infrared sketches and textual information. Additionally the emergence of large-scale models shows promising performance in various vision tasks but the foundation model in ReID is still blank. In response to these challenges a novel multimodal learning paradigm for ReID is introduced referred to as All-in-One (AIO) which harnesses a frozen pre-trained big model as an encoder enabling effective multimodal retrieval without additional fine-tuning. The diverse multimodal data in AIO are seamlessly tokenized into a unified space allowing the modality-shared frozen encoder to extract identity-consistent features comprehensively across all modalities. Furthermore a meticulously crafted ensemble of cross-modality heads is designed to guide the learning trajectory. AIO is the first framework to perform all-in-one ReID encompassing four commonly used modalities. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts showcasing exceptional performance in zero-shot and domain generalization scenarios. Code will be available at: https://github.com/lihe404/AIO.

Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server. B2KD faces challenges such as limited Internet exchange and edge-cloud disparity of data distributions. In this paper we formalize a two-step workflow consisting of deprivatization and distillation and theoretically provide a new optimization direction from logits to cell boundary different from direct logits alignment. With its guidance we propose a new method Mapping-Emulation KD (MEKD) that distills a black-box cumbersome model into a lightweight one. Our method does not differentiate between treating soft or hard responses and consists of: 1) deprivatization: emulating the inverse mapping of the teacher function with a generator and 2) distillation: aligning low-dimensional logits of the teacher and student models by reducing the distance of high-dimensional image points. For different teacher-student pairs our method yields inspiring distillation performance on various benchmarks and outperforms the previous state-of-the-art approaches.

In autonomous driving behavior prediction is fundamental for safe motion planning hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor attack against trajectory prediction models as a means of studying their potential vulnerabilities. Our attack affects the victim at training time via naturalistic hence stealthy poisoned samples crafted using a novel two-step approach. First the triggers are crafted by perturbing the trajectory of attacking vehicle and then disguised by transforming the scene using a bi-level optimization technique. The proposed attack does not depend on a particular model architecture and operates in a black-box manner thus can be effective without any knowledge of the victim model. We conduct extensive empirical studies using state-of-the-art prediction models on two benchmark datasets using metrics customized for trajectory prediction. We show that the proposed attack is highly effective as it can significantly hinder the performance of prediction models unnoticeable by the victims and efficient as it forces the victim to generate malicious behavior even under constrained conditions. Via ablative studies we analyze the impact of different attack design choices followed by an evaluation of existing defence mechanisms against the proposed attack.

Multi-view depth estimation has achieved impressive performance over various benchmarks. However almost all current multi-view systems rely on given ideal camera poses which are unavailable in many real-world scenarios such as autonomous driving. In this work we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To address this challenge we propose a single-view and multi-view fused depth estimation system which adaptively integrates high-confident multi-view and single-view results for both robust and accurate depth estimations. The adaptive fusion module performs fusion by dynamically selecting high-confidence regions between two branches based on a wrapping confidence map. Thus the system tends to choose the more reliable branch when facing textureless scenes inaccurate calibration dynamic objects and other degradation or challenging conditions. Our method outperforms state-of-the-art multi-view and fusion methods under robustness testing. Furthermore we achieve state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when given accurate pose estimations. Project website: https://github.com/Junda24/AFNet/

Trajectory prediction plays an important role in various applications including autonomous driving robotics and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets typically employing a standardized input duration. However a notable issue arises when these models are evaluated with varying observation lengths leading to a significant performance drop a phenomenon we term the Observation Length Shift. To address this issue we introduce a general and effective framework the FlexiLength Network (FLN) to enhance the robustness of existing trajectory prediction techniques against varying observation periods. Specifically FLN integrates trajectory data with diverse observation lengths incorporates FlexiLength Calibration (FLC) to acquire temporal invariant representations and employs FlexiLength Adaptation (FLA) to further refine these representations for more accurate future trajectory predictions. Comprehensive experiments on multiple datasets i.e. ETH/UCY nuScenes and Argoverse 1 demonstrate the effectiveness and flexibility of our proposed FLN framework.

Vision Transformer (ViT) has shown high potential in video recognition owing to its flexible design adaptable self-attention mechanisms and the efficacy of masked pre-training. Yet it remains unclear how to adapt these pre-trained short-term ViTs for temporal action detection (TAD) in untrimmed videos. The existing works treat them as off-the-shelf feature extractors for each short-trimmed snippet without capturing the fine-grained relation among different snippets in a broader temporal context. To mitigate this issue this paper focuses on designing a new mechanism for adapting these pre-trained ViT models as a unified long-form video transformer to fully unleash its modeling power in capturing inter-snippet relation while still keeping low computation overhead and memory consumption for efficient TAD. To this end we design effective cross-snippet propagation modules to gradually exchange short-term video information among different snippets from two levels. For inner-backbone information propagation we introduce a cross-snippet propagation strategy to enable multi-snippet temporal feature interaction inside the backbone. For post-backbone information propagation we propose temporal transformer layers for further clip-level modeling. With the plain ViT-B pre-trained with VideoMAE our end-to-end temporal action detector (ViT-TAD) yields a very competitive performance to previous temporal action detectors riching up to 69.5 average mAP on THUMOS14 37.40 average mAP on ActivityNet-1.3 and 17.20 average mAP on FineAction.

The pretraining-finetuning paradigm has gained popularity in various computer vision tasks. In this paradigm the emergence of active finetuning arises due to the abundance of large-scale data and costly annotation requirements. Active finetuning involves selecting a subset of data from an unlabeled pool for annotation facilitating subsequent finetuning. However the use of a limited number of training samples can lead to a biased distribution potentially resulting in model overfitting. In this paper we propose a new method called ActiveDC for the active finetuning tasks. Firstly we select samples for annotation by optimizing the distribution similarity between the subset to be selected and the entire unlabeled pool in continuous space. Secondly we calibrate the distribution of the selected samples by exploiting implicit category information in the unlabeled pool. The feature visualization provides an intuitive sense of the effectiveness of our approach to distribution calibration. We conducted extensive experiments on three image classification datasets with different sampling ratios. The results indicate that ActiveDC consistently outperforms the baseline performance in all image classification tasks. The improvement is particularly significant when the sampling ratio is low with performance gains of up to 10%. Our code will be released.

Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and training datasets to convert LiDAR scans into occupancy grid maps yield very low quality and subsequently present a novel approach using evidence theory that yields more accurate reconstructions. We demonstrate that these are superior by a large margin both qualitatively and quantitatively and that we additionally obtain meaningful uncertainty estimates. When converting the occupancy maps back to depth estimates and comparing them with the raw LiDAR measurements our method yields a MAE improvement of 30% to 52% on nuScenes and 53% on Waymo over other occupancy ground-truth data. Finally we use the improved occupancy maps to train a state-of-the-art occupancy prediction method and demonstrate that it improves the MAE by 25% on nuScenes.

We present MM-AU a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11727 in-the-wild ego-view accident videos each with temporally aligned text descriptions. We annotate over 2.23 million object boxes and 58650 pairs of video-based accident reasons covering 58 accident categories. MM-AU supports various accident understanding tasks particularly multimodal video diffusion to understand accident cause-effect chains for safe driving. With MM-AU we present an Abductive accident Video understanding framework for Safe Driving perception (AdVersa-SD). AdVersa-SD performs video diffusion via an Object-Centric Video Diffusion (OAVD) method which is driven by an abductive CLIP model. This model involves a contrastive interaction loss to learn the pair co-occurrence of normal near-accident accident frames with the corresponding text descriptions such as accident reasons prevention advice and accident categories. OAVD enforces the object region learning while fixing the content of the original frame background in video generation to find the dominant objects for certain accidents. Extensive experiments verify the abductive ability of AdVersa-SD and the superiority of OAVD against the state-of-the-art diffusion models. Additionally we provide careful benchmark evaluations for object detection and accident reason answering since AdVersa-SD relies on precise object and accident reason information.

Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies capturing network characteristics related to the final performance. However network rankings estimated by previous training-free NAS methods have shown weak correlations with the performance. To address this issue we propose AZ-NAS a novel approach that leverages the ensemble of various zero-cost proxies to enhance the correlation between a predicted ranking of networks and the ground truth substantially in terms of the performance. To achieve this we introduce four novel zero-cost proxies that are complementary to each other analyzing distinct traits of architectures in the views of expressivity progressivity trainability and complexity. The proxy scores can be obtained simultaneously within a single forward and backward pass making an overall NAS process highly efficient. In order to integrate the rankings predicted by our proxies effectively we introduce a non-linear ranking aggregation method that highlights the networks highly-ranked consistently across all the proxies. Experimental results conclusively demonstrate the efficacy and efficiency of AZ-NAS outperforming state-of-the-art methods on standard benchmarks all while maintaining a reasonable runtime cost.

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However objects encountered on the road exhibit a long-tailed distribution with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues efficiently curates data improves the model through auto-labeling and verifies the model through generation of diverse scenarios. This process operates iteratively allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms demonstrating our method's superior performance at a reduced cost.

A significant challenge facing current optical flow methods is the difficulty in generalizing them well to the real world. This is mainly due to the lack of large-scale real-world datasets and existing self-supervised methods are limited by indirect loss and occlusions resulting in fuzzy outcomes. To address this challenge we introduce a novel optical flow training framework: automatic data factory (ADF). ADF only requires RGB images as input to effectively train the optical flow network on the target data domain. Specifically we use advanced NeRF technology to reconstruct scenes from photo groups collected by a monocular camera and then calculate optical flow labels between camera pose pairs based on the rendering results. To eliminate erroneous labels caused by defects in the scene reconstructed by NeRF we screened the generated labels from multiple aspects such as optical flow matching accuracy radiation field confidence and depth consistency. The filtered labels can be directly used for network supervision. Experimentally the generalization ability of ADF on KITTI surpasses existing self-supervised optical flow and monocular scene flow algorithms. In addition ADF achieves impressive results in real-world zero-point generalization evaluations and surpasses most supervised methods.

Though diffusion models excel in image generation their step-by-step denoising leads to slow generation speeds. Consistency training addresses this issue with single-step sampling but often produces lower-quality generations and requires high training costs. In this paper we show that optimizing consistency training loss minimizes the Wasserstein distance between target and generated distributions. As timestep increases the upper bound accumulates previous consistency training losses. Therefore larger batch sizes are needed to reduce both current and accumulated losses. We propose Adversarial Consistency Training (ACT) which directly minimizes the Jensen-Shannon (JS) divergence between distributions at each timestep using a discriminator. Theoretically ACT enhances generation quality and convergence. By incorporating a discriminator into the consistency training framework our method achieves improved FID scores on CIFAR10 and ImageNet 64x64 and LSUN Cat 256x256 datasets retains zero-shot image inpainting capabilities and uses less than 1/6 of the original batch size and fewer than 1/2 of the model parameters and training steps compared to the baseline method this leads to a substantial reduction in resource consumption. Our code is available: https://github.com/kong13661/ACT