CVPR 2024 Papers

We present M&M VTO-a mix and match virtual try-on method that takes as input multiple garment images text description for garment layout and an image of a person. An example input includes: an image of a shirt an image of a pair of pants "rolled sleeves shirt tucked in" and an image of a person. The output is a visualization of how those garments (in the desired layout) would look like on the given person. Key contributions of our method are: 1) a single stage diffusion based model with no super resolution cascading that allows to mix and match multiple garments at 1024x512 resolution preserving and warping intricate garment details 2) architecture design (VTO UNet Diffusion Transformer) to disentangle denoising from person specific features allowing for a highly effective finetuning strategy for identity preservation (6MB model per individual vs 4GB achieved with e.g. dreambooth finetuning); solving a common identity loss problem in current virtual try-on methods 3) layout control for multiple garments via text inputs finetuned over PaLI-3 for virtual try-on task. Experimental results indicate that M&M VTO achieves state-of-the-art performance both qualitatively and quantitatively as well as opens up new opportunities for virtual try-on via language-guided and multi-garment try-on.

The recent advancements in text-to-3D generation mark a significant milestone in generative models unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios. While recent advancements in text-to-3D generation have shown promise they often fall short in rendering detailed and high-quality 3D models. This problem is especially prevalent as many methods base themselves on Score Distillation Sampling (SDS). This paper identifies a notable deficiency in SDS that it brings inconsistent and low-quality updating direction for the 3D model causing the over-smoothing effect. To address this we propose a novel approach called Interval Score Matching (ISM). ISM employs deterministic diffusing trajectories and utilizes interval-based score matching to counteract over-smoothing. Furthermore we incorporate 3D Gaussian Splatting into our text-to-3D generation pipeline. Extensive experiments show that our model largely outperforms the state-of-the-art in quality and training efficiency.

In this paper we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction. Conventional methods model outdoor scenes with fine-grained 3D grids but the sparsity of non-empty voxels introduces considerable spatial redundancy leading to potential overfitting risks. In contrast our approach leverages the intrinsic low-rank property of 3D occupancy data factorizing voxel representations into low-rank components to efficiently mitigate spatial redundancy without sacrificing performance. Specifically we present the Vertical-Horizontal (VH) decomposition block factorizes 3D tensors into vertical vectors and horizontal matrices. With our "decomposition-encoding-recovery" framework we encode 3D contexts with only 1/2D convolutions and poolings and subsequently recover the encoded compact yet informative context features back to voxel representations. Experimental results demonstrate that LowRankOcc achieves state-of-the-art performances in semantic scene completion on the SemanticKITTI dataset and 3D occupancy prediction on the nuScenes dataset.

Researchers in natural science need reliable methods for quantifying animal behavior. Recently numerous computer vision methods emerged to automate the process. However observing wild species at remote locations remains a challenging task due to difficult lighting conditions and constraints on power supply and data storage. Event cameras offer unique advantages for battery-dependent remote monitoring due to their low power consumption and high dynamic range capabilities. We use this novel sensor to quantify a behavior in Chinstrap penguins called ecstatic display. We formulate the problem as a temporal action detection task determining the start and end times of the behavior. For this purpose we recorded a colony of breeding penguins in Antarctica for several weeks and labeled event data on 16 nests. The developed method consists of a generator of candidate time intervals (proposals) and a classifier of the actions within them. The experiments show that the event cameras' natural response to motion is effective for continuous behavior monitoring and detection reaching a mean average precision (mAP) of 58% (which increases to 63% in good weather conditions). The results also demonstrate the robustness against various lighting conditions contained in the challenging dataset. The low-power capabilities of the event camera allow it to record significantly longer than with a conventional camera. This work pioneers the use of event cameras for remote wildlife observation opening new interdisciplinary opportunities. https:// tub-rip.github.io/ eventpenguins/

Low-resource settings are well-established in natural lan- guage processing where many languages lack sufficient data for deep learning at scale. However low-resource problems are under-explored in computer vision. In this paper we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data covering historic maps circuit diagrams and mechanical drawings. These low-resource settings all share three challenges: data scarcity fine-grained differences and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision we introduce one simple baseline per challenge. Specifically we i) enlarge the data space by generative models ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning data aug- mentation and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://xiaobai1217.github.io/ Low-Resource-Vision/.

For image super-resolution (SR) bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images merging them with super-resolved outputs for LR reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets outperforming existing methods. Our training regime is universally compatible requiring no network architecture modifications making it a practical solution for real-world SR applications.

Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently there is a lack of focus on guiding this delicate trade-off. In this study we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices providing insights into the tuning dynamics of existing methods. Building upon this understanding we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://github.com/zstarN70/RLRR.git.

The popularity of large-scale pre-training has promoted the development of medical foundation models. However some studies have shown that although foundation models exhibit strong general feature extraction capabilities their performance on specific tasks is still inferior to task-specific methods. In this paper we explore a new perspective called "Knowledge Decomposition" to improve the performance on specific medical tasks which deconstruct the foundation model into multiple lightweight expert models each dedicated to a particular task with the goal of improving specialization while concurrently mitigating resource expenditure. To accomplish the above objective we design a novel framework named Low-Rank Knowledge Decomposition (LoRKD) which explicitly separates graidents by incorporating low-rank expert modules and the efficient knowledge separation convolution. Extensive experimental results demonstrate that the decomposed models perform well in terms of performance and transferability even surpassing the original foundation models. Source code is available at: https://github.com/MediaBrain-SJTU/LoRKD

This paper focuses on the high computational complexity in Large Language Models (LLMs) a significant challenge in both natural language processing (NLP) and multi-modal tasks. We propose Low-Rank Approximation for Sparse At- tention (LoRA-Sparse) an innovative approach that strate- gically reduces this complexity. LoRA-Sparse introduces low-rank linear projection layers for sparse attention ap- proximation. It utilizes an order-mimic training methodol- ogy which is crucial for efficiently approximating the self- attention mechanism in LLMs. We empirically show that sparse attention not only reduces computational demands but also enhances model performance in both NLP and multi-modal tasks. This surprisingly shows that redundant attention in LLMs might be non-beneficial. We extensively validate LoRA-Sparse through rigorous empirical studies in both (NLP) and multi-modal tasks demonstrating its effec- tiveness and general applicability. Based on LLaMA and LLaVA models our methods can reduce more than half of the self-attention computation with even better performance than full-attention baselines.

The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods both in terms of rate-distortion (R-D) performance and in terms of delay and runtime. While most recent stereo video compression approaches have shown promising performance they compress left and right views sequentially leading to poor parallelization and runtime performance. This work presents Low-Latency neural codec for Stereo video Streaming (LLSS) a novel parallel stereo video coding method designed for fast and efficient low-latency stereo video streaming. Instead of using a sequential cross-view motion compensation like existing methods LLSS introduces a bidirectional feature shifting module to directly exploit mutual information among views and encode them effectively with a joint cross-view prior model for entropy coding. Thanks to this design LLSS processes left and right views in parallel minimizing latency; all while substantially improving R-D performance compared to both existing neural and conventional codecs.

Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life. In this paper we introduce Loose Inertial Poser a novel motion capture solution with high wearing comfortableness by integrating four Inertial Measurement Units (IMUs) into a loose-wear jacket. Specifically we address the challenge of scarce loose-wear IMU training data by proposing a Secondary Motion AutoEncoder (SeMo-AE) that learns to model and synthesize the effects of secondary motion between the skin and loose clothing on IMU data. SeMo-AE is leveraged to generate a diverse synthetic dataset of loose-wear IMU data to augment training for the pose estimation network and significantly improve its accuracy. For validation we collected a dataset with various subjects and 2 wearing styles (zipped and unzipped). Experimental results demonstrate that our approach maintains high-quality real-time posture estimation even in loose-wear scenarios.

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM) yet face challenges such as error accumulation during camera tracking resulting in distorted maps. In response we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Robust pose graph optimization is used to rigidly align the local submaps. As our representation is point based map corrections can be performed efficiently without the need to store the entire history of input frames used for mapping as typically required by methods employing a grid based mapping structure. Evaluation on the synthetic Replica and real-world TUM-RGBD and ScanNet datasets demonstrate competitive or superior performance in tracking mapping and rendering accuracy when compared to existing dense neural RGBD SLAM methods. Project page: notchla.github.io/Loopy-SLAM.

Audiovisual representation learning typically relies on the correspondence between sight and sound. However there are often multiple audio tracks that can correspond with a visual scene. Consider for example different conversations on the same crowded street. The effect of such counterfactual pairs on audiovisual representation learning has not been previously explored. To investigate this we use dubbed versions of movies and television shows to augment cross-modal contrastive learning. Our approach learns to represent alternate audio tracks differing only in speech similarly to the same video. Our results from a comprehensive set of experiments investigating different training strategies show this general approach improves performance on a range of downstream auditory and audiovisual tasks without majorly affecting linguistic task performance overall. These findings highlight the importance of considering speech variation when learning scene-level audiovisual correspondences and suggest that dubbed audio can be a useful augmentation technique for training audiovisual models toward more robust performance on diverse downstream tasks.

Automatic anomaly detection based on visual cues holds practical significance in various domains such as manufacturing and product quality assessment. This paper introduces a new conditional anomaly detection problem which involves identifying anomalies in a query image by comparing it to a reference shape. To address this challenge we have created a large dataset BrokenChairs-180K consisting of around 180K images with diverse anomalies geometries and textures paired with 8143 reference 3D shapes. To tackle this task we have proposed a novel transformer-based approach that explicitly learns the correspondence between the query image and reference 3D shape via feature alignment and leverages a customized attention mechanism for anomaly detection. Our approach has been rigorously evaluated through comprehensive experiments serving as a benchmark for future research in this domain.

Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments. At each navigation step the agent selects from possible candidate locations and then makes the move. For better navigation planning the lookahead exploration strategy aims to effectively evaluate the agent's next action by accurately anticipating the future environment of candidate locations. To this end some existing works predict RGB images for future environments while this strategy suffers from image distortion and high computational cost. To address these issues we propose the pre-trained hierarchical neural radiance representation model (HNR) to produce multi-level semantic features for future environments which are more robust and efficient than pixel-wise RGB reconstruction. Furthermore with the predicted future environmental representations our lookahead VLN model is able to construct the navigable future path tree and select the optimal path via efficient parallel evaluation. Extensive experiments on the VLN-CE datasets confirm the effectiveness of our method.

Look-Up Table (LUT) has recently gained increasing attention for restoring High-Quality (HQ) images from Low-Quality (LQ) observations thanks to its high computational efficiency achieved through a "space for time" strategy of caching learned LQ-HQ pairs. However incorporating multiple LUTs for improved performance comes at the cost of a rapidly growing storage size which is ultimately restricted by the allocatable on-device cache size. In this work we propose a novel LUT compression framework to achieve a better trade-off between storage size and performance for LUT-based image restoration models. Based on the observation that most cached LQ image patches are distributed along the diagonal of a LUT we devise a Diagonal-First Compression (DFC) framework where diagonal LQ-HQ pairs are preserved and carefully re-indexed to maintain the representation capacity while non-diagonal pairs are aggressively subsampled to save storage. Extensive experiments on representative image restoration tasks demonstrate that our DFC framework significantly reduces the storage size of LUT-based models (including our new design) while maintaining their performance. For instance DFC saves up to 90% of storage at a negligible performance drop for x4 super-resolution. The source code is available on GitHub: https://github.com/leenas233/DFC.

Anomaly detection (AD) aims to identify defective images and localize their defects (if any). Ideally AD models should be able to detect defects over many image classes; without relying on hard-coded class names that can be uninformative or inconsistent across datasets; learn without anomaly supervision; and be robust to the long-tailed distributions of real-world applications. To address these challenges we formulate the problem of long-tailed AD by introducing several datasets with different levels of class imbalance and metrics for performance evaluation. We then propose a novel method LTAD to detect defects from multiple and long-tailed classes without relying on dataset class names. LTAD combines AD by reconstruction and semantic AD modules. AD by reconstruction is implemented with a transformer-based reconstruction module. Semantic AD is implemented with a binary classifier which relies on learned pseudo class names and a pretrained foundation model. These modules are learned over two phases. Phase 1 learns the pseudo-class names and a variational autoencoder (VAE) for feature synthesis that augments the training data to combat long-tails. Phase 2 then learns the parameters of the reconstruction and classification modules of LTAD. Extensive experiments using the proposed long-tailed datasets show that LTAD substantially outperforms the state-of-the-art methods for most forms of dataset imbalance. The long-tailed dataset split is available at https://zenodo.org/records/10854201

Long-tail class incremental learning (LT-CIL) is designed to perpetually acquire novel knowledge from an imbalanced and perpetually evolving data stream while ensuring the retention of previously acquired knowledge. The existing method only re-balances data distribution and ignores exploring the potential relationship between different samples causing non-robust representations and even severe forgetting in classes with few samples. In this paper we constructed two parallel spaces simultaneously: 1) Sub-prototype space and 2) Reminiscence space to learn robust representations while alleviating forgetfulness. Concretely we advance the concept of the sub-prototype space which amalgamates insights from diverse classes. This integration facilitates the mutual complementarity of varied knowledge thereby augmenting the attainment of more robust representations. Furthermore we introduce the reminiscence space which encapsulates each class distribution aiming to constraint model optimization and mitigate the phenomenon of forgetting. The tandem utilization of the two parallel spaces effectively alleviates the adverse consequences associated with imbalanced data distribution preventing forgetting without needing replay examples. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various benchmarks.

Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function. However the assumption of a shared temperature between teacher and student implies a mandatory exact match between their logits in terms of logit range and variance. This side-effect limits the performance of student considering the capacity discrepancy between them and the finding that the innate logit relations of teacher are sufficient for student to learn. To address this issue we propose setting the temperature as the weighted standard deviation of logit and performing a plug-and-play Z-score pre-process of logit standardization before applying softmax and Kullback-Leibler divergence. Our pre-process enables student to focus on essential logit relations from teacher rather than requiring a magnitude match and can improve the performance of existing logit-based distillation methods. We also show a typical case where the conventional setting of sharing temperature between teacher and student cannot reliably yield the authentic distillation evaluation; nonetheless this challenge is successfully alleviated by our Z-score. We extensively evaluate our method for various student and teacher models on CIFAR-100 and ImageNet showing its significant superiority. The vanilla knowledge distillation powered by our pre-process can achieve favorable performance against state-of-the-art methods and other distillation variants can obtain considerable gain with the assistance of our pre-process. The codes pre-trained models and logs are released on Github.