CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions. Previous works realize it by simply integrating an extra textual module into the multi-object tracker. However they typically need to retrain the entire framework and have difficulties in optimization. In this work we propose an insertable Knowledge Unification Network termed iKUN to enable communication with off-the-shelf trackers in a plug-and-play manner. Concretely a knowledge unification module (KUM) is designed to adaptively extract visual features based on textual guidance. Meanwhile to improve the localization accuracy we present a neural version of Kalman filter (NKF) to dynamically adjust process noise and observation noise based on the current motion status. Moreover to address the problem of open-set long-tail distribution of textual descriptions a test-time similarity calibration method is proposed to refine the confidence score with pseudo frequency. Extensive experiments on Refer-KITTI dataset verify the effectiveness of our framework. Finally to speed up the development of RMOT we also contribute a more challenging dataset Refer-Dance by extending public DanceTrack dataset with motion and dressing descriptions. The codes and dataset are available at https://github.com/dyhBUPT/iKUN.
The key lie in semi-supervised semantic segmentation is how to fully exploit substantial unlabeled data to improve the model's generalization performance by resorting to constructing effective supervision signals. Most methods tend to directly apply contrastive learning to seek additional supervision to complement independent regular pixel-wise consistency regularization. However these methods tend not to be preferred ascribed to their complicated designs heavy memory footprints and susceptibility to confirmation bias. In this paper we analyze the bottlenecks exist in contrastive learning-based methods and offer a fresh perspective on inter-pixel correlations to construct more safe and effective supervision signals which is in line with the nature of semantic segmentation. To this end we develop a coherent RankMatch network including the construction of representative agents to model inter-pixel correlation beyond regular individual pixel-wise consistency and further unlock the potential of agents by modeling inter-agent relationships in pursuit of rank-aware correlation consistency. Extensive experimental results on multiple benchmarks including mitochondria segmentation demonstrate that RankMatch performs favorably against state-of-the-art methods. Particularly in the low-data regimes RankMatch achieves significant improvements.
The unprecedented capture and application of face images raise increasing concerns on anonymization to fight against privacy disclosure. Most existing methods may suffer from the problem of excessive change of the identity-independent information or insufficient identity protection. In this paper we present a new face anonymization approach by distracting the intrinsic and extrinsic identity attentions. On the one hand we anonymize the identity information in the feature space by distracting the intrinsic identity attention. On the other we anonymize the visual clues (i.e. appearance and geometry structure) by distracting the extrinsic identity attention. Our approach allows for flexible and intuitive manipulation of face appearance and geometry structure to produce diverse results and it can also be used to instruct users to perform personalized anonymization. We conduct extensive experiments on multiple datasets and demonstrate that our approach outperforms state-of-the-art methods.
Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However these methods heavily rely on the outputs of existing models leading to error accumulation in geometry and appearance that prevent the models from being used in various scenarios (e.g. outdoor and unreal scenarios). To address this limitation we generatively refine the newly generated local views by querying and aggregating global 3D information and then progressively generate the 3D scene. Specifically we employ a tri-plane features-based NeRF as a unified representation of the 3D scene to constrain global 3D consistency and propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior from 2D diffusion model as well as the global 3D information of the current scene. Our extensive experiments demonstrate that in comparison to previous methods our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.
This paper introduces a versatile multi-view inverse rendering framework with near- and far-field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse rendering our framework adopts a lightweight yet inclusive lighting model for different near- and far-field lights thus is able to make use of input images under varied lighting conditions available during capture. It leverages observations under each lighting to disentangle the intrinsic geometry and material from the external lighting using both neural radiance field rendering and physically-based surface rendering on the 3D implicit fields. After training the reconstructed scene is extracted to a textured triangle mesh for seamless integration into industrial rendering software for various applications. Quantitatively and qualitatively tested on synthetic and real-world scenes our method shows superiority to state-of-the-art multi-view inverse rendering methods in both speed and quality.
We propose RoHM an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train neural networks to directly regress motion in 3D or learn data-driven motion priors and combine them with optimization at test time. RoHM is a novel diffusion-based motion model that conditioned on noisy and occluded input data reconstructs complete plausible motions in consistent global coordinates. Given the complexity of the problem -- requiring one to address different tasks (denoising and infilling) in different solution spaces (local and global motion) -- we decompose it into two sub-tasks and learn two models one for global trajectory and one for local motion. To capture the correlations between the two we then introduce a novel conditioning module combining it with an iterative inference scheme. We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling. Extensive experiments on three popular datasets show that our method outperforms state-of-the-art approaches qualitatively and quantitatively while being faster at test time. The code is available at https://sanweiliti.github.io/ROHM/ROHM.html.
There has been significant attention to the research on dense video captioning which aims to automatically localize and caption all events within untrimmed video. Several studies introduce methods by designing dense video captioning as a multitasking problem of event localization and event captioning to consider inter-task relations. However addressing both tasks using only visual input is challenging due to the lack of semantic content. In this study we address this by proposing a novel framework inspired by the cognitive information processing of humans. Our model utilizes external memory to incorporate prior knowledge. The memory retrieval method is proposed with cross-modal video-to-text matching. To effectively incorporate retrieved text features the versatile encoder and the decoder with visual and textual cross-attention modules are designed. Comparative experiments have been conducted to show the effectiveness of the proposed method on ActivityNet Captions and YouCook2 datasets. Experimental results show promising performance of our model without extensive pretraining from a large video dataset. Our code is available at https://github.com/ailab-kyunghee/CM2_DVC.
Recently One-stage Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained increasing interest due to simplification over its cumbersome multi-stage counterpart. Limited by the inherent ambiguity of Class Activation Map (CAM) we observe that one-stage pipelines often encounter confirmation bias caused by incorrect CAM pseudo-labels impairing their final segmentation performance. Although recent works discard many unreliable pseudo-labels to implicitly alleviate this issue they fail to exploit sufficient supervision for their models. To this end we propose a dual student framework with trustworthy progressive learning (DuPL). Specifically we propose a dual student network with a discrepancy loss to yield diverse CAMs for each sub-net. The two sub-nets generate supervision for each other mitigating the confirmation bias caused by learning their own incorrect pseudo-labels. In this process we progressively introduce more trustworthy pseudo-labels to be involved in the supervision through dynamic threshold adjustment with an adaptive noise filtering strategy. Moreover we believe that every pixel even discarded from supervision due to its unreliability is important for WSSS. Thus we develop consistency regularization on these discarded regions providing supervision of every pixel.
Moreover we believe that every pixel even discarded from supervision due to its unreliability is important for WSSS. Thus we develop consistency regularization on these discarded regions providing supervision of every pixel. Experiment results demonstrate the superiority of the proposed DuPL over the recent state-of-the-art alternatives on PASCAL VOC 2012 and MS COCO datasets. Code is available at https://github.com/Wu0409/DuPL.
Deep Neural Networks (DNNs) have demonstrated remarkable performance across diverse domains and tasks with large-scale datasets. To reduce labeling costs for large-scale datasets semi-automated and crowdsourcing labeling methods are developed but their labels are inevitably noisy. Learning with Noisy Labels (LNL) approaches aim to train DNNs despite the presence of noisy labels. These approaches utilize the memorization effect to select correct labels and refine noisy ones which are then used for subsequent training. However these methods encounter a significant decrease in the model's generalization performance due to the inevitably existing noise labels. To overcome this limitation we propose a new approach to enhance learning with noisy labels by incorporating additional distribution information--structural labels. In order to leverage additional distribution information for generalization we employ a reverse k-NN which helps the model in achieving a better feature manifold and mitigating overfitting to noisy labels. The proposed method shows outperformed performance in multiple benchmark datasets with IDN and real-world noisy datasets.
Dynamic human rendering from video sequences has achieved remarkable progress by formulating the rendering as a mapping from static poses to human images. However existing methods focus on the human appearance reconstruction of every single frame while the temporal motion relations are not fully explored. In this paper we propose a new 4D motion modeling paradigm SurMo that jointly models the temporal dynamics and human appearances in a unified framework with three key designs: 1) Surface-based motion encoding that models 4D human motions with an efficient compact surface-based triplane. It encodes both spatial and temporal motion relations on the dense surface manifold of a statistical body template which inherits body topology priors for generalizable novel view synthesis with sparse training observations. 2) Physical motion decoding that is designed to encourage physical motion learning by decoding the motion triplane features at timestep t to predict both spatial derivatives and temporal derivatives at the next timestep t+1 in the training stage. 3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning.
3) 4D appearance decoding that renders the motion triplanes into images by an efficient volumetric surface-conditioned renderer that focuses on the rendering of body surfaces with motion learning conditioning. Extensive experiments validate the state-of-the-art performance of our new paradigm and illustrate the expressiveness of surface-based motion triplanes for rendering high-fidelity view-consistent humans with fast motions and even motion-dependent shadows. Our project page is at: https://taohuumd.github.io/projects/SurMo.
We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-attention proposed in prior work (e.g. MVDream) leads to content copying between views. Therefore we explicitly constrain the cross-view attention based on epipolar geometry. To further enhance 3D consistency we utilize Pl ?ucker coordinates derived from camera rays and inject them as positional encoding. This enables SPAD to reason over spatial proximity in 3D well. Compared to concurrent works that can only generate views at fixed azimuth and elevation (e.g. MVDream SyncDreamer) SPAD offers full camera control and achieves state-of-the-art results in novel view synthesis on unseen objects from the Objaverse and Google Scanned Objects datasets. Finally we demonstrate that text-to-3D generation using SPAD prevents the multi-face Janus issue.
Class-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge. A major challenge of CIL arises when applying to real-world data characterized by non-uniform distribution which introduces a dual imbalance problem involving (i) disparities between stored exemplars of old tasks and new class data (inter-phase imbalance) and (ii) severe class imbalances within each individual task (intra-phase imbalance). We show that this dual imbalance issue causes skewed gradient updates with biased weights in FC layers thus inducing over/under-fitting and catastrophic forgetting in CIL. Our method addresses it by reweighting the gradients towards balanced optimization and unbiased classifier learning. Additionally we observe imbalanced forgetting where paradoxically the instance-rich classes suffer higher performance degradation during CIL due to a larger amount of training data becoming unavailable in subsequent learning phases. To tackle this we further introduce a distribution-aware knowledge distillation loss to mitigate forgetting by aligning output logits proportionally with the distribution of lost training data. We validate our method on CIFAR-100 ImageNetSubset and Food101 across various evaluation protocols and demonstrate consistent improvements compared to existing works showing great potential to apply CIL in real-world scenarios with enhanced robustness and effectiveness.
Despite diffusion models having shown powerful abilities to generate photorealistic images generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together leading to a notably increased complexity of text-to-video generation (T2V). In this work we propose HiGen a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives i.e. structure level and content level. At the structure level we decompose the T2V task into two steps including spatial reasoning and temporal reasoning using a unified denoiser. Specifically we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level we extract two subtle cues from the content of the input video that can express motion and appearance changes respectively. These two cues then guide the model's training for generating videos enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. We have released our source code and models.
Recent advancements in large-scale pre-trained text-to-image models have led to remarkable progress in semantic image synthesis. Nevertheless synthesizing high-quality images with consistent semantics and layout remains a challenge. In this paper we propose the adaPtive LAyout-semantiC fusion modulE (PLACE) that harnesses pre-trained models to alleviate the aforementioned issues. Specifically we first employ the layout control map to faithfully represent layouts in the feature space. Subsequently we combine the layout and semantic features in a timestep-adaptive manner to synthesize images with realistic details. During fine-tuning we propose the Semantic Alignment (SA) loss to further enhance layout alignment. Additionally we introduce the Layout-Free Prior Preservation (LFP) loss which leverages unlabeled data to maintain the priors of pre-trained models thereby improving the visual quality and semantic consistency of synthesized images. Extensive experiments demonstrate that our approach performs favorably in terms of visual quality semantic consistency and layout alignment. The source code and model are available at \href https://github.com/cszy98/PLACE/tree/main  PLACE .
Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. However noise in real-world scenarios is often spatially correlated which causes many self-supervised algorithms that assume pixel-wise independent noise to perform poorly. Recent works have attempted to break noise correlation with downsampling or neighborhood masking. However denoising on downsampled subgraphs can lead to aliasing effects and loss of details due to a lower sampling rate. Furthermore the neighborhood masking methods either come with high computational complexity or do not consider local spatial preservation during inference. Through the analysis of existing methods we point out that the key to obtaining high-quality and texture-rich results in real-world self-supervised denoising tasks is to train at the original input resolution structure and use asymmetric operations during training and inference. Based on this we propose Asymmetric Tunable Blind-Spot Network (AT-BSN) where the blind-spot size can be freely adjusted thus better balancing noise correlation suppression and image local spatial destruction during training and inference. In addition we regard the pre-trained AT-BSN as a meta-teacher network capable of generating various teacher networks by sampling different blind-spots.
In addition we regard the pre-trained AT-BSN as a meta-teacher network capable of generating various teacher networks by sampling different blind-spots. We propose a blind-spot based multi-teacher distillation strategy to distill a lightweight network significantly improving performance. Experimental results on multiple datasets prove that our method achieves state-of-the-art and is superior to other self-supervised algorithms in terms of computational overhead and visual effects.
We present the first application of 3D Gaussian Splatting in monocular SLAM the most fundamental but the hardest setup for Visual SLAM. Our method which runs live at 3fps utilises Gaussians as the only 3D representation unifying the required representation for accurate efficient tracking mapping and high-quality rendering. Designed for challenging monocular settings our approach is seamlessly extendable to RGB-D SLAM when an external depth sensor is available. Several innovations are required to continuously reconstruct 3D scenes with high fidelity from a live camera. First to move beyond the original 3DGS algorithm which requires accurate poses from an offline Structure from Motion (SfM) system we formulate camera tracking for 3DGS using direct optimisation against the 3D Gaussians and show that this enables fast and robust tracking with a wide basin of convergence. Second by utilising the explicit nature of the Gaussians we introduce geometric verification and regularisation to handle the ambiguities occurring in incremental 3D dense reconstruction. Finally we introduce a full SLAM system which not only achieves state-of-the-art results in novel view synthesis and trajectory estimation but also reconstruction of tiny and even transparent objects.
The consistency training (CT)-based semi-supervised learning (SSL) bites state-of-the-art performance on SSL-based image classification. However the existing CT-based SSL methods do not highlight the non-Euclidean characteristics and class-wise varieties of embedding spaces in an SSL model thus they cannot fully utilize the effectiveness of CT. Thus we propose a metric tensor-based consistency regularization exploiting the class-variant geometrical structure of embeddings on the high-dimensional feature space. The proposed method not only minimizes the prediction discrepancy between different views of a given image but also estimates the intrinsic geometric curvature of embedding spaces by employing the global and local metric tensors. The global metric tensor is used to globally estimate the class-invariant embeddings from the whole data distribution while the local metric tensor is exploited to estimate the class-variant embeddings of each cluster. The two metric tensors are optimized by the consistency regularization based on the weak and strong augmentation strategy. The proposed method provides the highest classification accuracy on average compared to the existing state-of-the-art SSL methods on conventional datasets.
Understanding long real-world videos requires modeling of long-range visual dependencies. To this end we explore video-first architectures building on the common paradigm of transferring large-scale image--text models to video via shallow temporal fusion. However we expose two limitations to the approach: (1) decreased spatial capabilities likely due to poor video--language alignment in standard video datasets and (2) higher memory consumption bottlenecking the number of frames that can be processed. To mitigate the memory bottleneck we systematically analyze the memory/accuracy trade-off of various efficient methods: factorized attention parameter-efficient image-to-video adaptation input masking and multi-resolution patchification. Surprisingly simply masking large portions of the video (up to 75%) during contrastive pre-training proves to be one of the most robust ways to scale encoders to videos up to 4.3 minutes at 1 FPS. Our simple approach for training long video-to-text models which scales to 1B parameters does not add new architectural complexity and is able to outperform the popular paradigm of using much larger LLMs as an information aggregator over segment-based information on benchmarks with long-range temporal dependencies (YouCook2 EgoSchema).