CVPR 2024 论文列表

Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However the high costs and methodological limitations of ST necessitate a more robust predictive model. In response this paper introduces TRIPLEX a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features capturing cellular morphology at individual spots the local context around these spots and the global tissue organization. By integrating these features through an effective fusion strategy TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study conducted on three public ST datasets and supplemented with Visium data from 10X Genomics demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE) Mean Absolute Error (MAE) and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.

Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts its exploration in multimodal settings remains under explored. Some recent efforts have sought to transpose Euclidean multimodal learning techniques to hyperbolic spaces by adopting geodesic distance based contrastive losses. However we show both theoretically and empirically that such spatial proximity based contrastive loss significantly disrupts hierarchies in the latent space. To remedy this we advocate that the cross-modal representations should accept the inherent modality gap between text and images and introduce a novel approach to measure cross-modal similarity that does not enforce spatial proximity. Our approach show remarkable capabilities in preserving unimodal hierarchies while aligning the two modalities. Our experiments on a series of downstream tasks demonstrate that better latent structure emerges with our objective function while being superior in text-to-image and image-to-text retrieval tasks.

We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular it is often trained by uniformly sampling the training domain or through handcrafted heuristics. We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely we weigh the corresponding loss by a scalar. To implement our idea we use Langevin Monte-Carlo sampling. We show that by doing so regions with higher error are being selected more frequently leading to more than 2x improvement in convergence speed. The code and related resources for this study are publicly available at https://ubc-vision.github.io/nf-soft-mining/.

Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps. While this is a significant development most sampling methods still employ uniform time steps which is not optimal when using a small number of steps. To address this issue we propose a general framework for designing an optimization problem that seeks more appropriate time steps for a specific numerical ODE solver for DPMs. This optimization problem aims to minimize the distance between the ground-truth solution to the ODE and an approximate solution corresponding to the numerical solver. It can be efficiently solved using the constrained trust region method taking less than 15 seconds. Our extensive experiments on both unconditional and conditional sampling using pixel- and latent-space DPMs demonstrate that when combined with the state-of-the-art sampling method UniPC our optimized time steps significantly improve image generation performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet compared to using uniform time steps.

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case or one scaled and oriented correspondence given a known vertical direction. Nowadays assuming a known direction is not particularly restrictive as modern consumer devices such as smartphones or drones are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences our solvers need a smaller minimal sample reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://github.com/danini/absolute-pose-from-oriented-and-scaled-features .

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.

Recent advances in diffusion models have successfully enabled text-guided image inpainting. While it seems straightforward to extend such editing capability into the video domain there have been fewer works regarding text-guided video inpainting. Given a video a masked region at its initial frame and an editing prompt it requires a model to do infilling at each frame following the editing guidance while keeping the out-of-mask region intact. There are three main challenges in text-guided video inpainting: (i) temporal consistency of the edited video (ii) supporting different inpainting types at different structural fidelity levels and (iii) dealing with variable video length. To address these challenges we introduce Any-Length Video Inpainting with Diffusion Model dubbed as AVID. At its core our model is equipped with effective motion modules and adjustable structure guidance for fixed-length video inpainting. Building on top of that we propose a novel Temporal MultiDiffusion sampling pipeline with a middle-frame attention guidance mechanism facilitating the generation of videos with any desired duration. Our comprehensive experiments show our model can robustly deal with various inpainting types at different video duration ranges with high quality.

With the rapid growth in deepfake video content we require improved and generalizable methods to detect them. Most existing detection methods either use uni-modal cues or rely on supervised training to capture the dissonance between the audio and visual modalities. While the former disregards the audio-visual correspondences entirely the latter predominantly focuses on discerning audio-visual cues within the training corpus thereby potentially overlooking correspondences that can help detect unseen deepfakes. We present Audio-Visual Feature Fusion (AVFF) a two-stage cross-modal learning method that explicitly captures the correspondence between the audio and visual modalities for improved deepfake detection. The first stage pursues representation learning via self-supervision on real videos to capture the intrinsic audio-visual correspondences. To extract rich cross-modal representations we use contrastive learning and autoencoding objectives and introduce a novel audio-visual complementary masking and feature fusion strategy. The learned representations are tuned in the second stage where deepfake classification is pursued via supervised learning on both real and fake videos. Extensive experiments and analysis suggest that our novel representation learning paradigm is highly discriminative in nature. We report 98.6% accuracy and 99.1% AUC on the FakeAVCeleb dataset outperforming the current audio-visual state-of-the-art by 14.9% and 9.9% respectively.

This paper proposes a novel direct Audio-Visual Speech to Audio-Visual Speech Translation (AV2AV) framework where the input and output of the system are multimodal (i.e. audio and visual speech). With the proposed AV2AV two key advantages can be brought: 1) We can perform real-like conversations with individuals worldwide in a virtual meeting by utilizing our own primary languages. In contrast to Speech-to-Speech Translation (A2A) which solely translates between audio modalities the proposed AV2AV directly translates between audio-visual speech. This capability enhances the dialogue experience by presenting synchronized lip movements along with the translated speech. 2) We can improve the robustness of the spoken language translation system. By employing the complementary information of audio-visual speech the system can effectively translate spoken language even in the presence of acoustic noise showcasing robust performance. To mitigate the problem of the absence of a parallel AV2AV translation dataset we propose to train our spoken language translation system with the audio-only dataset of A2A. This is done by learning unified audio-visual speech representations through self-supervised learning in advance to train the translation system. Moreover we propose an AV-Renderer that can generate raw audio and video in parallel. It is designed with zero-shot speaker modeling thus the speaker in source audio-visual speech can be maintained at the target translated audio-visual speech. The effectiveness of AV2AV is evaluated with extensive experiments in a many-to-many language translation setting. Demo page is available on choijeongsoo.github.io/av2av.

Accurate estimation of Room Impulse Response (RIR) which captures an environment's acoustic properties is important for speech processing and AR/VR applications. We propose AV-RIR a novel multi-modal multi-task learning approach to accurately estimate the RIR from a given reverberant speech signal and the visual cues of its corresponding environment. AV-RIR builds on a novel neural codec-based architecture that effectively captures environment geometry and materials properties and solves speech dereverberation as an auxiliary task by using multi-task learning. We also propose Geo-Mat features that augment material information into visual cues and CRIP that improves late reverberation components in the estimated RIR via image-to-RIR retrieval by 86%. Empirical results show that AV-RIR quantitatively outperforms previous audio-only and visual-only approaches by achieving 36% - 63% improvement across various acoustic metrics in RIR estimation. Additionally it also achieves higher preference scores in human evaluation. As an auxiliary benefit dereverbed speech from AV-RIR shows competitive performance with the state-of-the-art in various spoken language processing tasks and outperforms reverberation time error score in the real-world AVSpeech dataset. Qualitative examples of both synthesized reverberant speech and enhanced speech are available online https://www.youtube.com/watch?v=tTsKhviukAE.

Facial action unit (AU) intensity plays a pivotal role in quantifying fine-grained expression behaviors which is an effective condition for facial expression manipulation. However publicly available datasets containing intensity annotations for multiple AUs remain severely limited often featuring a restricted number of subjects. This limitation places challenges to the AU intensity manipulation in images due to disentanglement issues leading researchers to resort to other large datasets with pretrained AU intensity estimators for pseudo labels. In addressing this constraint and fully leveraging manual annotations of AU intensities for precise manipulation we introduce AUEditNet. Our proposed model achieves impressive intensity manipulation across 12 AUs trained effectively with only 18 subjects. Utilizing a dual-branch architecture our approach achieves comprehensive disentanglement of facial attributes and identity without necessitating additional loss functions or implementing with large batch sizes. This approach offers a potential solution to achieve desired facial attribute editing despite the dataset's limited subject count. Our experiments demonstrate AUEditNet's superior accuracy in editing AU intensities affirming its capability in disentangling facial attributes and identity within a limited subject pool. AUEditNet allows conditioning by either intensity values or target images eliminating the need for constructing AU combinations for specific facial expression synthesis. Moreover AU intensity estimation as a downstream task validates the consistency between real and edited images confirming the effectiveness of our proposed AU intensity manipulation method.

Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To address this we propose ASH an animatable Gaussian splatting approach for photorealistic rendering of dynamic humans in real time. We parameterize the clothed human as animatable 3D Gaussians which can be efficiently splatted into image space to generate the final rendering. However naively learning the Gaussian parameters in 3D space poses a severe challenge in terms of compute. Instead we attach the Gaussians onto a deformable character model and learn their parameters in 2D texture space which allows leveraging efficient 2D convolutional architectures that easily scale with the required number of Gaussians. We benchmark ASH with competing methods on pose-controllable avatars demonstrating that our method outperforms existing real-time methods by a large margin and shows comparable or even better results than offline methods.

In the evolving landscape of computer vision foundation models have emerged as pivotal tools exhibiting exceptional adaptability to a myriad of tasks. Among these the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However SAM like its counterparts encounters limitations in specific niche applications prompting a quest for enhancement strategies that do not compromise its inherent capabilities. This paper introduces ASAM a novel methodology that amplifies SAM's performance through adversarial tuning. We harness the potential of natural adversarial examples inspired by their successful implementation in natural language processing. By utilizing a stable diffusion model we augment a subset (1%) of the SA-1B dataset generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations thereby preserving the integrity of the segmentation task. The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications. The results of our extensive evaluations confirm that ASAM establishes new benchmarks in segmentation tasks thereby contributing to the advancement of foundational models in computer vision. Our project page is in https://asam2024.github.io/.

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains and their performances degrade significantly while applied to a distinct domain. To this end we propose to leverage the cutting-edge foundation model the Segment Anything Model (SAM) for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data which primarily comprise natural scene images and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work we introduce APSeg a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS) which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes allowing features to be transformed into a more stable domain-agnostic space. Additionally a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings respectively.

While real-world anime super-resolution (SR) has gained increasing attention in the SR community existing methods still adopt techniques from the photorealistic domain. In this paper we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR. First we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline we introduce the Anime Production-oriented Image (API) dataset. In addition we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines. In addition we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark showing our method outperforms state-of-the-art anime dataset-trained approaches.

Recent progress in human shape learning shows that neural implicit models are effective in generating 3D human surfaces from limited number of views and even from a single RGB image. However existing monocular approaches still struggle to recover fine geometric details such as face hands or cloth wrinkles. They are also easily prone to depth ambiguities that result in distorted geometries along the camera optical axis. In this paper we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy. Our model learns geometric details from both multi-resolution pixel-aligned and voxel-aligned features to leverage depth information and enable spatial relationships mitigating depth ambiguities. We further enhance the quality of the reconstructed shape by introducing a depth-supervision strategy which improves the accuracy of the signed distance field estimation of points that lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms state-of-the-art works that use RGB surface normals point cloud or RGB-D data as input. In addition we introduce ANIM-Real a new multi-modal dataset comprising high-quality scans paired with consumer-grade RGB-D camera and our protocol to fine-tune ANIM enabling high-quality reconstruction from real-world human capture.

Recently pre-trained vision-language models (e.g. CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made to improve few-shot ability of CLIP key factors on the effectiveness of existing methods have not been well studied limiting further exploration of CLIP's potential in few-shot learning. In this paper we first introduce a unified formulation to analyze CLIP-based few-shot learning methods from a perspective of logit bias which encourages us to learn an effective logit bias for further improving performance of CLIP-based few-shot learning methods. To this end we disassemble three key components involved in computation of logit bias (i.e. logit features logit predictor and logit fusion) and empirically analyze the effect on performance of few-shot classification. Based on analysis of key components this paper proposes a novel AMU-Tuning method to learn effective logit bias for CLIP-based few-shot classification. Specifically our AMU-Tuning predicts logit bias by exploiting the appropriate Auxiliary features which are fed into an efficient feature-initialized linear classifier with Multi-branch training. Finally an Uncertainty-based fusion is developed to incorporate logit bias into CLIP for few-shot classification. The experiments are conducted on several widely used benchmarks and the results show AMU-Tuning clearly outperforms its counterparts while achieving state-of-the-art performance of CLIP-based few-shot learning without bells and whistles.