CVPR 2024 Papers

Video and audio content creation serves as the core technique for the movie industry and professional users. Recently existing diffusion-based methods tackle video and audio generation separately which hinders the technique transfer from academia to industry. In this work we aim at filling the gap with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus instead of training the giant models from scratch we propose to bridge the existing strong models with a shared latent representation space. Specifically we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions we show the superior performance of our method on joint video-audio generation visual-steered audio generation and audio-steered visual generation tasks. The project website can be found at \href https://yzxing87.github.io/Seeing-and-Hearing/ https://yzxing87.github.io/Seeing-and-Hearing/ .

Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity are difficult to provide the high precision localization in resource-constrained devices. To this end we propose a novel filter-based VINS framework named SchurVINS (SV) which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically we first formulate the full residual model where Gradient Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://github.com/bytedance/SchurVINS.

Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet prevailing approaches focus on pre-training 2D representations being sub-optimal to deal with occlusions and accurately localize objects in complex 3D scenes. Meanwhile 3D representation learning has been limited to single-object understanding. To address these limitations we introduce a novel 3D pre-training framework for robotics named SUGAR that captures semantic geometric and affordance properties of objects through 3D point clouds. We underscore the importance of cluttered scenes in 3D representation learning and automatically construct a multi-object dataset benefiting from cost-free supervision in simulation. SUGAR employs a versatile transformer-based model to jointly address five pre-training tasks namely cross-modal knowledge distillation for semantic learning masked point modeling to understand geometry structures grasping pose synthesis for object affordance 3D instance segmentation and referring expression grounding to analyze cluttered scenes. We evaluate our learned representation on three robotic-related tasks namely zero-shot 3D object recognition referring expression grounding and language-driven robotic manipulation. Experimental results show that SUGAR's 3D representation outperforms state-of-the-art 2D and 3D representations.

We present a new dataset called Real Acoustic Fields (RAF) that captures real acoustic room data from multiple modalities. The dataset includes high-quality and densely captured room impulse response data paired with multi-view images and precise 6DoF pose tracking data for sound emitters and listeners in the rooms. We used this dataset to evaluate existing methods for novel-view acoustic synthesis and impulse response generation which previously relied on synthetic data. In our evaluation we thoroughly assessed existing audio and audio-visual models against multiple criteria and proposed settings to enhance their performance on real-world data. We also conducted experiments to investigate the impact of incorporating visual data (i.e. images and depth) into neural acoustic field models. Additionally we demonstrated the effectiveness of a simple sim2real approach where a model is pre-trained with simulated data and fine-tuned with sparse real-world data resulting in significant improvements in the few-shot learning approach. RAF is the first dataset to provide densely captured room acoustic data making it an ideal resource for researchers working on audio and audio-visual neural acoustic field modeling techniques.

Diagnosis in histopathology requires a global whole slide images (WSIs) analysis requiring pathologists to compound evidence from different WSI patches. The gigapixel scale of WSIs poses a challenge for histopathology multi-modal models. Training multi-model models for histopathology requires instruction tuning datasets which currently contain information for individual image patches without a spatial grounding of the concepts within each patch and without a wider view of the WSI. To bridge this gap we introduce QUILT-INSTRUCT a large-scale dataset of 107131 histopathology-specific instruction question/answer pairs grounded within diagnostically relevant image patches that make up the WSI. Our dataset is collected by leveraging educational histopathology videos from YouTube which provides spatial localization of narrations by automatically extracting the narrators' cursor positions. QUILT-INSTRUCT supports contextual reasoning by extracting diagnosis and supporting facts from the entire WSI. Using QUILT-INSTRUCT we train QUILT-LLAVA which can reason beyond the given single image patch enabling diagnostic reasoning across patches. To evaluate QUILT-LLAVA we propose a comprehensive evaluation dataset created from 985 images and 1283 human-generated question-answers. We also thoroughly evaluate QUILT-LLAVA using public histopathology datasets where QUILT-LLAVA significantly outperforms SOTA by over 10% on relative GPT-4 score and 4% and 9% on open and closed set VQA.

Recent advances in large-scale pretraining have yielded visual foundation models with strong capabilities. Not only can recent models generalize to arbitrary images for their training task their intermediate representations are useful for other visual tasks such as detection and segmentation. Given that such models can classify delineate and localize objects in 2D we ask whether they also represent their 3D structure? In this work we analyze the 3D awareness of visual foundation models. We posit that 3D awareness implies that representations (1) encode the 3D structure of the scene and (2) consistently represent the surface across views. We conduct a series of experiments using task-specific probes and zero-shot inference procedures on frozen features. Our experiments reveal several limitations of the current models. Our code and analysis can be found at https://github.com/mbanani/probe3d.

With the rapidly increasing demand for oriented object detection (OOD) recent research involving weakly-supervised detectors for learning rotated box (RBox) from the horizontal box (HBox) has attracted more and more attention. In this paper we explore a more challenging yet label-efficient setting namely single point-supervised OOD and present our approach called Point2RBox. Specifically we propose to leverage two principles: 1) Synthetic pattern knowledge combination: By sampling around each labeled point on the image we spread the object feature to synthetic visual patterns with known boxes to provide the knowledge for box regression. 2) Transform self-supervision: With a transformed input image (e.g. scaled/rotated) the output RBoxes are trained to follow the same transformation so that the network can perceive the relative size/rotation between objects. The detector is further enhanced by a few devised techniques to cope with peripheral issues e.g. the anchor/layer assignment as the size of the object is not available in our point supervision setting. To our best knowledge Point2RBox is the first end-to-end solution for point-supervised OOD. In particular our method uses a lightweight paradigm yet it achieves a competitive performance among point-supervised alternatives 41.05%/27.62%/80.01% on DOTA/DIOR/HRSC datasets.

The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities giving rise to vision large language models (VLLMs). However effectively harnessing LLMs for intricate visual perception tasks such as detection and segmentation remains a challenge. Conventional approaches achieve this by transforming perception signals (e.g. bounding boxes segmentation masks) into sequences of discrete tokens which struggle with the precision errors and introduces further complexities for training. In this paper we present a novel end-to-end framework named PerceptionGPT which represent the perception signals using LLM's dynamic token embedding. Specifically we leverage lightweight encoders and decoders to handle the perception signals in LLM's embedding space which takes advantage of the representation power of the high-dimensional token embeddings. Our approach significantly eases the training difficulties associated with the discrete representations in prior methods. Furthermore owing to our compact representation the inference speed is also greatly boosted. Consequently PerceptionGPT enables accurate flexible and efficient handling of complex perception signals. We validate the effectiveness of our approach through extensive experiments. The results demonstrate significant improvements over previous methods with only 4% trainable parameters and less than 25% training time.

The large-scale visual pretraining has significantly improve the performance of large vision models. However we observe the low FLOPs pitfall that the existing low-FLOPs models cannot benefit from large-scale pretraining. In this paper we introduce a novel design principle termed ParameterNet aimed at augmenting the number of parameters in large-scale visual pretraining models while minimizing the increase in FLOPs. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The ParameterNet approach allows low-FLOPs networks to take advantage of large-scale visual pretraining. Furthermore we extend the ParameterNet concept to the language domain to enhance inference results while preserving inference speed. Experiments on the large-scale ImageNet-22K have shown the superiority of our ParameterNet scheme. For example ParameterNet-600M can achieve higher accuracy than the widely-used Swin Transformer (81.6% vs. 80.9%) and has much lower FLOPs (0.6G vs. 4.5G). The code will be released at https://parameternet.github.io/.

The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone in order to form a global descriptor for each image. In this context we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors) which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a dustbin cluster designed to selectively discard features deemed non-informative enhancing the overall descriptor quality. Additionally we leverage and fine-tune DINOv2 as a backbone which provides enhanced description power for the local features and dramatically reduces the required training time. As a result our single-stage method not only surpasses single-stage baselines in public VPR datasets but also surpasses two-stage methods that add a re-ranking with significantly higher cost.

Determining the location of an image anywhere on Earth is a complex visual task which makes it particularly relevant for evaluating computer vision algorithms. Determining the location of an image anywhere on Earth is a complex visual task which makes it particularly relevant for evaluating computer vision algorithms. Yet the absence of standard large-scale open-access datasets with reliably localizable images has limited its potential. To address this issue we introduce OpenStreetView-5M a large-scale open-access dataset comprising over 5.1 million geo-referenced street view images covering 225 countries and territories. In contrast to existing benchmarks we enforce a strict train/test separation allowing us to evaluate the relevance of learned geographical features beyond mere memorization. To demonstrate the utility of our dataset we conduct an extensive benchmark of various state-of-the-art image encoders spatial representations and training strategies. All associated codes and models can be found at https://github.com/gastruc/osv5m.

In Visual Place Recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses. As is typical for image retrieval problems a feature extractor maps the query and reference images to a feature space where a nearest neighbor search is then performed. However till recently little attention has been given to quantifying the confidence that a retrieved reference image is a correct match. Highly certain but incorrect retrieval can lead to catastrophic failure of VPR-based localization pipelines. This work compares for the first time the main approaches for estimating the image-matching uncertainty including the traditional retrieval-based uncertainty estimation more recent data-driven aleatoric uncertainty estimation and the compute-intensive geometric verification. We further formulate a simple baseline method "SUE" which unlike the other methods considers the freely-available poses of the reference images in the map. Our experiments reveal that a simple L2-distance between the query and reference descriptors is already a better estimate of image-matching uncertainty than current data-driven approaches. SUE outperforms the other efficient uncertainty estimation methods and its uncertainty estimates complement the computationally expensive geometric verification approach. Future works for uncertainty estimation in VPR should consider the baselines discussed in this work.

Ultra-fine-grained visual categorization (Ultra-FGVC) aims at distinguishing highly similar sub-categories within fine-grained objects such as different soybean cultivars. Compared to traditional fine-grained visual categorization Ultra-FGVC encounters more hurdles due to the small inter-class and large intra-class variation. Given these challenges relying on human annotation for Ultra-FGVC is impractical. To this end our work introduces a novel task termed Ultra-Fine-Grained Novel Class Discovery (UFG-NCD) which leverages partially annotated data to identify new categories of unlabeled images for Ultra-FGVC. To tackle this problem we devise a Region-Aligned Proxy Learning (RAPL) framework which comprises a Channel-wise Region Alignment (CRA) module and a Semi-Supervised Proxy Learning (SemiPL) strategy. The CRA module is designed to extract and utilize discriminative features from local regions facilitating knowledge transfer from labeled to unlabeled classes. Furthermore SemiPL strengthens representation learning and knowledge transfer with proxy-guided supervised learning and proxy-guided contrastive learning. Such techniques leverage class distribution information in the embedding space improving the mining of subtle differences between labeled and unlabeled ultra-fine-grained classes. Extensive experiments demonstrate that RAPL significantly outperforms baselines across various datasets indicating its effectiveness in handling the challenges of UFG-NCD. Code is available at https://github.com/SSDUT-Caiyq/UFG-NCD.

We investigate a fundamental aspect of machine vision: the measurement of features by revisiting clustering one of the most classic approaches in machine learning and data analysis. Existing visual feature extractors including ConvNets ViTs and MLPs represent an image as rectangular regions. Though prevalent such a grid-style paradigm is built upon engineering practice and lacks explicit modeling of data distribution. In this work we propose feature extraction with clustering (FEC) a conceptually elegant yet surprisingly ad-hoc interpretable neural clustering framework which views feature extraction as a process of selecting representatives from data and thus automatically captures the underlying data distribution. Given an image FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives. Such an iterative working mechanism is implemented in the form of several neural layers and the final representatives can be used for downstream tasks. The cluster assignments across layers which can be viewed and inspected by humans make the forward process of FEC fully transparent and empower it with promising ad-hoc interpretability. Extensive experiments on various visual recognition models and tasks verify the effectiveness generality and interpretability of FEC. We expect this work will provoke a rethink of the current de facto grid-style paradigm.

A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and appearance of a scene. We here ask the question whether we can transfer the appearance from a source NeRF onto a target 3D geometry in a semantically meaningful way such that the resulting new NeRF retains the target geometry but has an appearance that is an analogy to the source NeRF. To this end we generalize classic image analogies from 2D images to NeRFs. We leverage correspondence transfer along semantic affinity that is driven by semantic features from large pre-trained 2D image models to achieve multi-view consistent appearance transfer. Our method allows exploring the mix-and-match product space of 3D geometry and appearance. We show that our method outperforms traditional stylization-based methods and that a large majority of users prefer our method over several typical baselines. Project page: https://mfischer-ucl.github.io/nerf_analogies

Despite the commercial abundance of UAVs aerial data acquisition remains challenging and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally the color content of the scenes solar zenith angle and population density of different geographies influence the data diversity. These factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground view data including the open-world foundational models. To pave the way for a transformative era of aerial detection we present Multiview Aerial Visual RECognition (MAVREC) a video dataset where we record synchronized scenes from different perspectives --- ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences more than 0.5 million frames and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial view dataset and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC we recognize that augmenting object detectors with ground view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance aerial detection.

Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks. Recent efforts have been made to equip MLLMs with visual perceiving and grounding capabilities. However there still remains a gap in providing fine-grained pixel-level perceptions and extending interactions beyond text-specific inputs. In this work we propose \bf AnyRef a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references such as texts boxes images or audio. This innovation empowers users with greater flexibility to engage with the model beyond textual and regional prompts without modality-specific designs. Through our proposed refocusing mechanism the generated grounding output is guided to better focus on the referenced object implicitly incorporating additional pixel-level supervision. This simple modification utilizes attention scores generated during the inference of LLM eliminating the need for extra computations while exhibiting performance enhancements in both grounding masks and referring expressions. With only publicly available training data our model achieves state-of-the-art results across multiple benchmarks including diverse modality referring segmentation and region-level referring expression generation. Code and models are available at https://github.com/jwh97nn/AnyRef

Multiple clustering has gained significant attention in recent years due to its potential to reveal multiple hidden structures of data from different perspectives. The advent of deep multiple clustering techniques has notably advanced the performance by uncovering complex patterns and relationships within large datasets. However a major challenge arises as users often do not need all the clusterings that algorithms generate and figuring out the one needed requires a substantial understanding of each clustering result. Traditionally aligning a user's brief keyword of interest with the corresponding vision components was challenging but the emergence of multi-modal and large language models (LLMs) has begun to bridge this gap. In response given unlabeled target visual data we propose Multi-Map a novel method employing a multi-modal proxy learning process. It leverages CLIP encoders to extract coherent text and image embeddings with GPT-4 integrating users' interests to formulate effective textual contexts. Moreover reference word constraint and concept-level constraint are designed to learn the optimal text proxy according to the user's interest. Multi-Map not only adeptly captures a user's interest via a keyword but also facilitates identifying relevant clusterings. Our extensive experiments show that Multi-Map consistently outperforms state-of-the-art methods in all benchmark multi-clustering vision tasks. Our code is available at https://github.com/Alexander-Yao/Multi-MaP.