CVPR 2024 论文列表

Modeling object dynamics with a neural network is an important problem with numerous applications. Most recent work has been based on graph neural networks. However physics happens in 3D space where geometric information potentially plays an important role in modeling physical phenomena. In this work we propose a novel U-net architecture based on continuous point convolution which naturally embeds information from 3D coordinates and allows for multi-scale feature representations with established downsampling and upsampling procedures. Bottleneck layers in the downsampled point clouds lead to better long-range interaction modeling. Besides the flexibility of point convolutions allows our approach to generalize to sparsely sampled points from mesh vertices and dynamically generate features on important interaction points on mesh faces. Experimental results demonstrate that our approach significantly improves the state-of-the-art especially in scenarios that require accurate gravity or collision reasoning.

The challenge of open-vocabulary recognition lies in the model has no clue of new categories it is applied to. Existing works have proposed different methods to embed category cues into the model e.g. through few-shot fine-tuning providing category names or textual descriptions to Vision-Language Models. Fine-tuning is time-consuming and degrades the generalization capability. Textual descriptions could be ambiguous and fail to depict visual details. This paper tackles open-vocabulary recognition from a different perspective by referring to multi-modal clues composed of textual descriptions and exemplar images. Our method named OVMR adopts two innovative components to pursue a more robust category cues embedding. A multi-modal classifier is first generated by dynamically complementing textual descriptions with image exemplars. A preference-based refinement module is hence applied to fuse uni-modal and multi-modal classifiers with the aim to alleviate issues of low-quality exemplar images or textual descriptions. The proposed OVMR is a plug-and-play module and works well with exemplar images randomly crawled from the Internet. Extensive experiments have demonstrated the promising performance of OVMR e.g. it outperforms existing methods across various scenarios and setups. Codes are publicly available at \href https://github.com/Zehong-Ma/OVMR https://github.com/Zehong-Ma/OVMR .

In the realm of food computing segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients the emergence of new ingredients and the high annotation costs associated with large food segmentation datasets. Existing approaches primarily utilize a closed-vocabulary and static text embeddings setting. These methods often fall short in effectively handling the ingredients particularly new and diverse ones. In response to these limitations we introduce OVFoodSeg a framework that adopts an open-vocabulary setting and enhances text embeddings with visual context. By integrating vision-language models (VLMs) our approach enriches text embedding with image-specific information through two innovative modules e.g. an image-to-text learner FoodLearner and an Image-Informed Text Encoder. The training process of OVFoodSeg is divided into two stages: the pre-training of FoodLearner and the subsequent learning phase for segmentation. The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task. By addressing the deficiencies of previous models OVFoodSeg demonstrates a significant improvement achieving an 4.9% increase in mean Intersection over Union (mIoU) on the FoodSeg103 dataset setting a new milestone for food image segmentation.

Recent advances in Iterative Vision-and-Language Navigation(IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end we propose OVER-NAV which aims to go over and beyond the current arts of IVLN techniques. In particular we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data we further introduce a structured representation coded Omnigraph to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.

In this paper we propose a novel framework to fully exploit the potential of a single vector for scene text recognition (STR). Different from previous sequence-to-sequence methods that rely on a sequence of visual tokens to represent scene text images we prove that just one token is enough to characterize the entire text image and achieve accurate text recognition. Based on this insight we introduce a new paradigm for STR called One Token rEcognizer (OTE). Specifically we implement an image-to-vector encoder to extract the fine-grained global semantics eliminating the need for sequential features. Furthermore an elegant yet potent vector-to-sequence decoder is designed to adaptively diffuse global semantics to corresponding character locations enabling both autoregressive and non-autoregressive decoding schemes. By executing decoding within a high-level representational space our vector-to-sequence (V2S) approach avoids the alignment issues between visual tokens and character embeddings prevalent in traditional sequence-to-sequence methods. Remarkably due to introducing character-wise fine-grained information such global tokens also boost the performance of scene text retrieval tasks. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of our method by achieving new state-of-the-art results on various public STR benchmarks. Our code is available at https://github.com/Xu-Jianjun/OTE.

Due to the resource-intensive nature of training vision-language models on expansive video data a majority of studies have centered on adapting pre-trained image-language models to the video domain. Dominant pipelines propose to tackle the visual discrepancies with additional temporal learners while overlooking the substantial discrepancy for web-scaled descriptive narratives and concise action category names leading to less distinct semantic space and potential performance limitations. In this work we prioritize the refinement of text knowledge to facilitate generalizable video recognition. To address the limitations of the less distinct semantic space of category names we prompt a large language model (LLM) to augment action class names into Spatio-Temporal Descriptors thus bridging the textual discrepancy and serving as a knowledge base for general recognition. Moreover to assign the best descriptors with different video instances we propose Optimal Descriptor Solver forming the video recognition problem as solving the optimal matching flow across frame-level representations and descriptors. Comprehensive evaluations in zero-shot few-shot and fully supervised video recognition highlight the effectiveness of our approach. Our best model achieves a state-of-the-art zero-shot accuracy of 75.1% on Kinetics-600.

Hallucination posed as a pervasive challenge of multi-modal large language models (MLLMs) has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources incurring inevitable additional costs. In this paper we present OPERA a novel MLLM decoding method grounded in an Over-trust Penalty and a Retrospection-Allocation strategy serving as a nearly free lunch to alleviate the hallucination issue without additional data knowledge or training. Our approach begins with an interesting observation that most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix i.e. MLLMs tend to generate new tokens by focusing on a few summary tokens but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue along with a rollback strategy that retrospects the presence of summary tokens in the previously generated tokens and re-allocate the token selection if necessary. With extensive experiments OPERA shows significant hallucination-mitigating performance on different MLLMs and metrics proving its effectiveness and generality. Our code is available at: https://github.com/shikiw/OPERA.

Trajectory prediction is fundamental in computer vision and autonomous driving particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range physical obstructions and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns as they can result in missing essential non-visible objects. To bridge this gap we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj) setting a new benchmark for future research.

We have recently seen tremendous progress in realistic text-to-motion generation. Yet the existing methods often fail or produce implausible motions with unseen text inputs which limits the applications. In this paper we present OMG a novel framework which enables compelling motion generation from zero-shot open-vocabulary text prompts. Our key idea is to carefully tailor the pretrain-then-finetune paradigm into the text-to-motion generation. At the pre-training stage our model improves the generation ability by learning the rich out-of-domain inherent motion traits. To this end we scale up a large unconditional diffusion model up to 1B parameters so as to utilize the massive unlabeled motion data up to over 20M motion instances. At the subsequent fine-tuning stage we introduce motion ControlNet which incorporates text prompts as conditioning information through a trainable copy of the pre-trained model and the proposed novel Mixture-of-Controllers (MoC) block. MoC block adaptively recognizes various ranges of the sub-motions with a cross-attention mechanism and processes them separately with the text-token-specific experts. Such a design effectively aligns the CLIP token embeddings of text prompts to various ranges of compact and expressive motion features. Extensive experiments demonstrate that our OMG achieves significant improvements over the state-of-the-art methods on zero-shot text-to-motion generation. Project page: https://tr3e.github.io/omg-page.

In this work we address various segmentation tasks each traditionally tackled by distinct or partially unified models. We propose OMG-Seg One Model that is Good enough to efficiently and effectively handle all the segmentation tasks including image semantic instance and panoptic segmentation as well as their video counterparts open vocabulary settings prompt-driven interactive segmentation like SAM and video object segmentation. To our knowledge this is the first model to handle all these tasks in one model and achieve satisfactory performance. We show that OMG-Seg a transformer-based encoder-decoder architecture with task-specific queries and outputs can support over ten distinct segmentation tasks and yet significantly reduce computational and parameter overhead across various tasks and datasets. We rigorously evaluate the inter-task influences and correlations during co-training. Code and models are available at https://github.com/lxtGH/OMG-Seg.

In this paper we delve into the creation of one-shot hand avatars attaining high-fidelity and drivable hand representations swiftly from a single image. With the burgeoning domains of the digital human the need for quick and personalized hand avatar creation has become increasingly critical. Existing techniques typically require extensive input data and may prove cumbersome or even impractical in certain scenarios. To enhance accessibility we present a novel method OHTA (One-shot Hand avaTAr) that enables the creation of detailed hand avatars from merely one image. OHTA tackles the inherent difficulties of this data-limited problem by learning and utilizing data-driven hand priors. Specifically we design a hand prior model initially employed for 1) learning various hand priors with available data and subsequently for 2) the inversion and fitting of the target identity with prior knowledge. OHTA demonstrates the capability to create high-fidelity hand avatars with consistent animatable quality solely relying on a single image. Furthermore we illustrate the versatility of OHTA through diverse applications encompassing text-to-avatar conversion hand editing and identity latent space manipulation.

Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines which typically consist of object detection temporal association and multi-relation classification. However these methods exhibit inherent limitations due to the separation of multiple stages and independent optimization of these sub-problems may yield sub-optimal solutions. To remedy these limitations we propose a one-stage end-to-end framework termed OED which streamlines the DSGG pipeline. This framework reformulates the task as a set prediction problem and leverages pair-wise features to represent each subject-object pair within the scene graph. Moreover another challenge of DSGG is capturing temporal dependencies we introduce a Progressively Refined Module (PRM) for aggregating temporal context without the constraints of additional trackers or handcrafted trajectories enabling end-to-end optimization of the network. Extensive experiments conducted on the Action Genome benchmark demonstrate the effectiveness of our design. The code and models are available at https://github.com/guanw-pku/OED.

Abstract In recent years text-image joint pre-training techniques have shown promising results in various tasks. However in Optical Character Recognition (OCR) tasks aligning text instances with their corresponding text regions in images poses a challenge as it requires effective alignment between text and OCR-Text (referring to the text in images as OCR-Text to distinguish from the text in natural language) rather than a holistic understanding of the overall image content. In this paper we propose a new pre-training method called OCR-Text Destylization Modeling (ODM) that transfers diverse styles of text found in images to a uniform style based on the text prompt. With ODM we achieve better alignment between text and OCR-Text and enable pre-trained models to adapt to the complex and diverse styles of scene text detection and spotting tasks. Additionally we have designed a new labeling generation method specifically for ODM and combined it with our proposed Text-Controller module to address the challenge of annotation costs in OCR tasks allowing a larger amount of unlabeled data to participate in pre-training. Extensive experiments on multiple public datasets demonstrate that our method significantly improves performance and outperforms current pre-training methods in scene text detection and spotting tasks. Code is available at https://github.com/PriNing/ODM.

State-of-the-art models on contemporary 3D segmentation benchmarks like ScanNet consume and label dataset-provided 3D point clouds obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain forego large-scale 2D pre-training and outperform alternatives that featurize the posed RGB-D multiview images instead. The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures. In this paper we challenge this view and propose ODIN (Omni-Dimensional INstance segmentation) a model that can segment and label both 2D RGB images and 3D point clouds using a transformer architecture that alternates between 2D within-view and 3D cross-view information fusion. Our model differentiates 2D and 3D feature operations through the positional encodings of the tokens involved which capture pixel coordinates for 2D patch tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art performance on ScanNet200 Matterport3D and AI2THOR 3D instance segmentation benchmarks and competitive performance on ScanNet S3DIS and COCO. It outperforms all previous works by a wide margin when the sensed 3D point cloud is used in place of the point cloud sampled from 3D mesh. When used as the 3D perception engine in an instructable embodied agent architecture it sets a new state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and checkpoints can be found at the project website: https://odin-seg.github.io.

Unpaired image dehazing (UID) holds significant research importance due to the challenges in acquiring haze/clear image pairs with identical backgrounds. This paper proposes a novel method for UID named Orthogonal Decoupling Contrastive Regularization (ODCR). Our method is grounded in the assumption that an image consists of both haze-related features which influence the degree of haze and haze-unrelated features such as texture and semantic information. ODCR aims to ensure that the haze-related features of the dehazing result closely resemble those of the clear image while the haze-unrelated features align with the input hazy image. To accomplish the motivation Orthogonal MLPs optimized geometrically on the Stiefel manifold are proposed which can project image features into an orthogonal space thereby reducing the relevance between different features. Furthermore a task-driven Depth-wise Feature Classifier (DWFC) is proposed which assigns weights to the orthogonal features based on the contribution of each channel's feature in predicting whether the feature source is hazy or clear in a self-supervised fashion. Finally a Weighted PatchNCE (WPNCE) loss is introduced to achieve the pulling of haze-related features in the output image toward those of clear images while bringing haze-unrelated features close to those of the hazy input. Extensive experiments demonstrate the superior performance of our ODCR method on UID.

The scarcity of ground-truth labels poses one major challenge in developing optical flow estimation models that are both generalizable and robust. While current methods rely on data augmentation they have yet to fully exploit the rich information available in labeled video sequences. We propose OCAI a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between. Utilizing a forward warping approach OCAI employs occlusion awareness to resolve ambiguities in pixel values and fills in missing values by leveraging the forward-backward consistency of optical flows. Additionally we introduce a teacher-student style semi-supervised learning method on top of the interpolated frames. Using a pair of unlabeled frames and the teacher model's predicted optical flow we generate interpolated frames and flows to train a student model. The teacher's weights are maintained using Exponential Moving Averaging of the student. Our evaluations demonstrate perceptually superior interpolation quality and enhanced optical flow accuracy on established benchmarks such as Sintel and KITTI.

We present OAKINK2 a dataset of bimanual object manipulation tasks for complex daily activities. In pursuit of constructing the complex tasks into a structured representation OAKINK2 introduces three level of abstraction to organize the manipulation tasks: Affordance Primitive Task and Complex Task. OAKINK2 features on an object-centric perspective for decoding the complex tasks treating them as a sequence of object affordance fulfillment. The first level Affordance outlines the functionalities that objects in the scene can afford the second level Primitive Task describes the minimal interaction units that humans interact with the object to achieve its affordance and the third level Complex Task illustrates how Primitive Tasks are composed and interdependent. OAKINK2 dataset provides multi-view image streams and precise pose annotations for the human body hands and various interacting objects. This extensive collection supports applications such as interaction reconstruction and motion synthesis. Based on the 3-level abstraction of OAKINK2 we explore a task-oriented framework for Complex Task Completion (CTC). CTC aims to generate a sequence of bimanual manipulation to achieve task objectives. Within the CTC framework we employ Large Language Models (LLMs) to decompose the complex task objectives into sequences of Primitive Tasks and have developed a Motion Fulfillment Model that generates bimanual hand motion for each Primitive Task. OAKINK2 datasets and models are available at https://oakink.net/v2.

The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models especially in 3D semantic segmentation. However sparse CNNs are still valuable networks due to their efficiency treasure and ease of application. In this work we reexamine the design distinctions and test the limits of what a sparse CNN can achieve. We discover that the key credit to the performance difference is adaptivity. Specifically we propose two key components i.e. adaptive receptive fields (spatially) and adaptive relation to bridge the gap. This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs) a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost. Without any self-attention modules OA-CNNs favorably surpass point transformers in terms of accuracy in both indoor and outdoor scenes with much less latency and memory cost. Notably it achieves 76.1% 78.9% and 70.6% mIoU on ScanNet v2 nuScenes and SemanticKITTI validation benchmarks respectively while maintaining at most 5x better speed than transformer counterparts. This revelation highlights the potential of pure sparse CNNs to outperform transformer-related networks. Our code is built upon Pointcept which is available at https://github.com/Pointcept/Pointcept.

In this paper we address single image-based novel view synthesis (NVS) by firstly integrating view-dependent effects (VDE) into the process. Our approach leverages camera motion priors to model VDE treating negative disparity as the representation of these effects in the scene. By identifying that specularities align with camera motion we infuse VDEs into input images by aggregating pixel colors along the negative depth region of epipolar lines. Additionally we introduce a relaxed volumetric rendering approximation enhancing efficiency by computing densities in a single pass for NVS from single images. Notably our method learns single-image NVS from image sequences alone making it a fully self-supervised learning approach that requires no depth or camera pose annotations. We present extensive experimental results and show that our proposed method can learn NVS with VDEs outperforming the SOTA single-view NVS methods on the RealEstate10k and MannequinChallenge datasets. Visit our project site https://kaist-viclab.github.io/monovde-site.