CVPR 2024 Papers

Short-form UGC video platforms like Kwai and TikTok have been an emerging and irreplaceable mainstream media form thriving on user-friendly engagement and kaleidoscope creation etc. However the advancing content generation modes e.g. special effects and sophisticated processing workflows e.g. de-artifacts have introduced significant challenges to recent UGC video quality assessment: (i) the ambiguous contents hinder the identification of quality-determined regions. (ii) the diverse and complicated hybrid distortions are hard to distinguish. To tackle the above challenges and assist in the development of short-form videos we establish the first large-scale Kwai short Video database for Quality assessment termed KVQ which comprises 600 user-uploaded short videos and 3600 processed videos through the diverse practical processing workflows including pre-processing transcoding and enhancement. Among them the absolute quality score of each video and partial ranking score among indistinguish samples are provided by a team of professional researchers specializing in image processing. Based on this database we propose the first short-form video quality evaluator i.e. KSVQE which enables the quality evaluator to identify the quality-determined semantics with the content understanding of large vision language models (i.e. CLIP) and distinguish the distortions with the distortion under- standing module. Experimental results have shown the effectiveness of KSVQE on our KVQ database and popular VQA databases. The project can be found at https: //lixinustc.github.io/projects/KVQ/.

This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer) which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q K V vectors in their self-attention mechanisms are all based on simple linear mapping. We propose two prior attention modules namely Kinematics Prior Attention (KPA) and Trajectory Prior Attention (TPA) to take advantage of the known anatomical structure of the human body and motion trajectory information to facilitate effective learning of global dependencies and features in the multi-head self-attention. KPA models kinematic relationships in the human body by constructing a topology of kinematics while TPA builds a trajectory topology to learn the information of joint motion trajectory across frames. Yielding Q K V vectors with prior knowledge the two modules enable KTPFormer to model both spatial and temporal correlations simultaneously. Extensive experiments on three benchmarks (Human3.6M MPI-INF-3DHP and HumanEva) show that KTPFormer achieves superior performance in comparison to state-of-the-art methods. More importantly our KPA and TPA modules have lightweight plug-and-play designs and can be integrated into various transformer-based networks (i.e. diffusion-based) to improve the performance with only a very small increase in the computational overhead. The code is available at: https://github.com/JihuaPeng/KTPFormer.

In the field of deep point cloud understanding KPConv is a unique architecture that uses kernel points to locate convolutional weights in space instead of relying on Multi-Layer Perceptron (MLP) encodings. While it initially achieved success it has since been surpassed by recent MLP networks that employ updated designs and training strategies. Building upon the kernel point principle we present two novel designs: KPConvD (depthwise KPConv) a lighter design that enables the use of deeper architectures and KPConvX an innovative design that scales the depthwise convolutional weights of KPConvD with kernel attention values. Using KPConvX with a modern architecture and training strategy we are able to outperform current state-of-the-art approaches on the ScanObjectNN Scannetv2 and S3DIS datasets. We validate our design choices through ablation studies and release our code and models.

In this paper we present KP-RED a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly match the target. Unlike existing dense matching based methods that typically struggle with noisy partial scans we propose to leverage category-consistent sparse keypoints to naturally handle both full and partial object scans. Specifically we first employ a lightweight retrieval module to establish a keypoint-based embedding space measuring the similarity among objects by dynamically aggregating deformation-aware local-global features around extracted keypoints. Objects that are close in the embedding space are considered similar in geometry. Then we introduce the neural cage-based deformation module that estimates the influence vector of each keypoint upon cage vertices inside its local support region to control the deformation of the retrieved shape. Extensive experiments on the synthetic dataset PartNet and the real-world dataset Scan2CAD demonstrate that KP-RED surpasses existing state-of-the-art approaches by a large margin. Codes and trained models will be released in https://github.com/lolrudy/KP-RED.

2D keypoints are commonly used as an additional cue to refine estimated 3D human meshes. Current methods optimize the pose and shape parameters with a reprojection loss on the provided 2D keypoints. Such an approach while simple and intuitive has limited effectiveness because the optimal solution is hard to find in ambiguous parameter space and may sacrifice depth. Additionally divergent gradients from distal joints complicate and deviate the refinement of proximal joints in the kinematic chain. To address these we introduce Kinematic-Tree Rotation (KITRO) a novel mesh refinement strategy that explicitly models depth and human kinematic-tree structure. KITRO treats refinement from a bone-wise perspective. Unlike previous methods which perform gradient-based optimizations our method calculates bone directions in closed form. By accounting for the 2D pose bone length and parent joint's depth the calculation results in two possible directions for each child joint. We then use a decision tree to trace binary choices for all bones along the human skeleton's kinematic-tree to select the most probable hypothesis. Our experiments across various datasets and baseline models demonstrate that KITRO significantly improves 3D joint estimation accuracy and achieves an ideal 2D fit simultaneously. Our code available at: https://github.com/MartaYang/KITRO.

DETR is a novel end-to-end transformer architecture object detector which significantly outperforms classic detectors when scaling up. In this paper we focus on the compression of DETR with knowledge distillation. While knowledge distillation has been well-studied in classic detectors there is a lack of researches on how to make it work effectively on DETR. We first provide experimental and theoretical analysis to point out that the main challenge in DETR distillation is the lack of consistent distillation points. Distillation points refer to the corresponding inputs of the predictions for student to mimic which have different formulations in CNN detector and DETR and reliable distillation requires sufficient distillation points which are consistent between teacher and student. Based on this observation we propose the first general knowledge distillation paradigm for DETR(KD-DETR) with consistent distillation points sampling for both homogeneous and heterogeneous distillation. Specifically we decouple detection and distillation tasks by introducing a set of specialized object queries to construct distillation points for DETR. We further propose a general-to-specific distillation points sampling strategy to explore the extensibility of KD-DETR. Extensive experiments validate the effectiveness and generalization of KD-DETR. For both single-scale DAB-DETR and multis-scale Deformable DETR and DINO KD-DETR boost the performance of student model with improvements of 2.6%-5.2%. We further extend KD-DETR to heterogeneous distillation and achieves 2.1% improvement by distilling the knowledge from DINO to Faster R-CNN with ResNet-50 which is comparable with homogeneous distillation methods.

Video transformers have become the de facto standard for human action recognition yet their exclusive reliance on the RGB modality still limits their adoption in certain domains. One such domain is Activities of Daily Living (ADL) where RGB alone is not sufficient to distinguish between visually similar actions or actions observed from multiple viewpoints. To facilitate the adoption of video transformers for ADL we hypothesize that the augmentation of RGB with human pose information known for its sensitivity to fine-grained motion and multiple viewpoints is essential. Consequently we introduce the first Pose Induced Video Transformer: PI-ViT (or π-ViT) a novel approach that augments the RGB representations learned by video transformers with 2D and 3D pose information. The key elements of π-ViT are two plug-in modules 2D Skeleton Induction Module and 3D Skeleton Induction Module that are responsible for inducing 2D and 3D pose information into the RGB representations. These modules operate by performing pose-aware auxiliary tasks a design choice that allows π-ViT to discard the modules during inference. Notably π-ViT achieves the state-of-the-art performance on three prominent ADL datasets encompassing both real-world and large-scale RGB-D datasets without requiring poses or additional computational overhead at inference.

Structural model pruning is a prominent approach used for reducing the computational cost of Convolutional Neural Networks (CNNs) before their deployment on resource-constrained devices. Yet the majority of proposed ideas require a pretrained model before pruning which is costly to secure. In this paper we propose a novel structural pruning approach to jointly learn the weights and structurally prune architectures of CNN models. The core element of our method is a Reinforcement Learning (RL) agent whose actions determine the pruning ratios of the CNN model's layers and the resulting model's accuracy serves as its reward. We conduct the joint training and pruning by iteratively training the model's weights and the agent's policy and we regularize the model's weights to align with the selected structure by the agent. The evolving model's weights result in a dynamic reward function for the agent which prevents using prominent episodic RL methods with stationary environment assumption for our purpose. We address this challenge by designing a mechanism to model the complex changing dynamics of the reward function and provide a representation of it to the RL agent. To do so we take a learnable embedding for each training epoch and employ a recurrent model to calculate a representation of the changing environment. We train the recurrent model and embeddings using a decoder model to reconstruct observed rewards. Such a design empowers our agent to effectively leverage episodic observations along with the environment representations to learn a proper policy to determine performant sub-networks of the CNN model. Our extensive experiments on CIFAR-10 and ImageNet using ResNets and MobileNets demonstrate the effectiveness of our method.

Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods.

3D human generation is increasingly significant in various applications. However the direct use of 2D generative methods in 3D generation often results in losing local details while methods that reconstruct geometry from generated images struggle with global view consistency. In this work we introduce Joint2Human a novel method that leverages 2D diffusion models to generate detailed 3D human geometry directly ensuring both global structure and local details. To achieve this we employ the Fourier occupancy field (FOF) representation enabling the direct generation of 3D shapes as preliminary results with 2D generative models. With the proposed high-frequency enhancer and the multi-view recarving strategy our method can seamlessly integrate the details from different views into a uniform global shape. To better utilize the 3D human prior and enhance control over the generated geometry we introduce a compact spherical embedding of 3D joints. This allows for an effective guidance of pose during the generation process. Additionally our method can generate 3D humans guided by textual inputs. Our experimental results demonstrate the capability of our method to ensure global structure local details high resolution and low computational cost simultaneously. More results and the code can be found on our project page at http://cic.tju.edu.cn/faculty/likun/projects/Joint2Human.

Multi-task learning has become increasingly popular in the machine learning field but its practicality is hindered by the need for large labeled datasets. Most multi-task learning methods depend on fully labeled datasets wherein each input example is accompanied by ground-truth labels for all target tasks. Unfortunately curating such datasets can be prohibitively expensive and impractical especially for dense prediction tasks which require per-pixel labels for each image. With this in mind we propose Joint-Task Regularization (JTR) an intuitive technique which leverages cross-task relations to simultaneously regularize all tasks in a single joint-task latent space to improve learning when data is not fully labeled for all tasks. JTR stands out from existing approaches in that it regularizes all tasks jointly rather than separately in pairs---therefore it achieves linear complexity relative to the number of tasks while previous methods scale quadratically. To demonstrate the validity of our approach we extensively benchmark our method across a wide variety of partially labeled scenarios based on NYU-v2 Cityscapes and Taskonomy.

Human-object contact serves as a strong cue to understand how humans physically interact with objects. Nevertheless it is not widely explored to utilize human-object contact information for the joint reconstruction of 3D human and object from a single image. In this work we present a novel joint 3D human-object reconstruction method (CONTHO) that effectively exploits contact information between humans and objects. There are two core designs in our system: 1) 3D-guided contact estimation and 2) contact-based 3D human and object refinement. First for accurate human-object contact estimation CONTHO initially reconstructs 3D humans and objects and utilizes them as explicit 3D guidance for contact estimation. Second to refine the initial reconstructions of 3D human and object we propose a novel contact-based refinement Transformer that effectively aggregates human features and object features based on the estimated human-object contact. The proposed contact-based refinement prevents the learning of erroneous correlation between human and object which enables accurate 3D reconstruction. As a result our CONTHO achieves state-of-the-art performance in both human-object contact estimation and joint reconstruction of 3D human and object. The codes are available in https://github.com/dqj5182/CONTHO_RELEASE.

Leveraging few-shot datasets in prompt learning for Vision-Language Models eliminates the need for manual prompt engineering while highlighting the necessity of accurate annotations for the labels. However high-level or complex label noise challenges prompt learning for Vision-Language Models. Aiming at this issue we propose a new framework for improving its robustness. Specifically we introduce the Joint Adaptive Partitioning for Label Refurbishment (JoAPR) a structured framework encompassing two key steps. 1) Data Partitioning where we differentiate between clean and noisy data using joint adaptive thresholds. 2) Label Refurbishment where we correct the labels based on the partition outcomes before retraining the network. Our comprehensive experiments confirm that JoAPR substantially enhances the robustness of prompt learning for Vision-Language Models against label noise offering a promising direction for future research.

Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes finding applications in various domains. To achieve the personalization capability existing methods rely on finetuning a text-to-image foundation model on a user's custom dataset which can be non-trivial for general users resource-intensive and time-consuming. Despite attempts to develope finetuning-free methods their generation quality is much lower compared to their finetuning counterparts. In this paper we propose Joint-Image Diffusion (\jedi) an effective technique for learning a finetuning-free personalization model. Our key idea is to learn the joint distribution of multiple related text-image pairs that share a common subject. To facilitate learning we propose a scalable synthetic dataset generation technique. Once trained our model enables fast and easy personalization at test time by simply using reference images as input during the sampling process. Our approach does not require any expensive optimization process or additional modules and can faithfully preserve the identity represented by any number of reference images. Experimental results show that our model achieves state-of-the-art generation quality both quantitatively and qualitatively significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.

The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems unifying various vision-language (VL) tasks by instruction tuning. However due to the enormous diversity in input-output formats in the vision domain existing general-purpose models fail to successfully integrate segmentation and multi-image inputs with coarse-level tasks into a single framework. In this work we introduce VistaLLM a powerful visual system that addresses coarse- and fine grained VL tasks over single and multiple input images using a unified framework. VistaLLM utilizes an instruction-guided image tokenizer that filters global embeddings using task descriptions to extract compressed and refined features from numerous images. Moreover VistaLLM employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences significantly improving over previously used uniform sampling. To bolster the desired capability of VistaLLM we curate CoinIt a comprehensive coarse-to-fine instruction tuning dataset with 6.8M samples. We also address the lack of multi-image grounding datasets by introducing a novel task AttCoSeg (Attribute-level Co Segmentation) which boosts the model's reasoning and grounding capability over multiple input images. Extensive experiments on a wide range of V- and VL tasks demonstrate the effectiveness of VistaLLM by achieving consistent state-of-the-art performance over strong baselines across many downstream tasks. Our project page can be found at https://shramanpramanick.github.io/VistaLLM/

Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short necessitating a comprehensive approach that considers individual behaviour intra-group dynamics and social group levels for a thorough understanding. To address dataset limitations this paper introduces JRDB-Social an extension of JRDB. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts JRDB-Social provides annotations at three levels: individual attributes intra-group interactions and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models we evaluated our benchmark to explore their capacity to decipher social human behaviour.

Autonomous robot systems have attracted increasing research attention in recent years where environment understanding is a crucial step for robot navigation human-robot interaction and decision. Real-world robot systems usually collect visual data from multiple sensors and are required to recognize numerous objects and their movements in complex human-crowded settings. Traditional benchmarks with their reliance on single sensors and limited object classes and scenarios fail to provide the comprehensive environmental understanding robots need for accurate navigation interaction and decision-making. As an extension of JRDB dataset we unveil JRDB-PanoTrack a novel open-world panoptic segmentation and tracking benchmark towards more comprehensive environmental perception. JRDB-PanoTrack includes (1) various data involving indoor and outdoor crowded scenes as well as comprehensive 2D and 3D synchronized data modalities; (2) high-quality 2D spatial panoptic segmentation and temporal tracking annotations with additional 3D label projections for further spatial understanding; (3) diverse object classes for closed- and open-world recognition benchmarks with OSPA-based metrics for evaluation. Extensive evaluation of leading methods shows significant challenges posed by our dataset.

We propose a practical approach to JPEG image decoding utilizing a local implicit neural representation with continuous cosine formulation. The JPEG algorithm significantly quantizes discrete cosine transform (DCT) spectra to achieve a high compression rate inevitably resulting in quality degradation while encoding an image. We have designed a continuous cosine spectrum estimator to address the quality degradation issue that restores the distorted spectrum. By leveraging local DCT formulations our network has the privilege to exploit dequantization and upsampling simultaneously. Our proposed model enables decoding compressed images directly across different quality factors using a single pre-trained model without relying on a conventional JPEG decoder. As a result our proposed network achieves state-of-the-art performance in flexible color image JPEG artifact removal tasks. Our source code is available at https://github.com/WooKyoungHan/JDEC

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet despite the performance gains contributed by large vision and language pretraining recent investigations find that most--if not all--our state-of-the-art vision-language models struggle at compositionality. They are unable to distinguish between images of "a girl in white facing a man in black" and "a girl in black facing a man in white". Moreover prior work suggests that compositionality doesn't arise with scale: larger model sizes or training data don't help. This paper develops a new iterated training algorithm that incentivizes compositionality. We draw on decades of cognitive science research that identifies cultural transmission--the need to teach a new generation--as a necessary inductive prior that incentivizes humans to develop compositional languages. Specifically we reframe vision-language contrastive learning as the Lewis Signaling Game between a vision agent and a language agent and operationalize cultural transmission by iteratively resetting one of the agent's weights during training. After every iteration this training paradigm induces representations that become "easier to learn" a property of compositional languages: e.g. our model trained on CC3M and CC12M improves standard CLIP by 4.7% 4.0% respectfully in the SugarCrepe benchmark.