mtybilly/Instruct2DS huggingface dataset:
Stratified Avatar Generation from Sparse Observations
Authors: Han Feng, Wenchao Ma, Quankai Gao, Xianwei Zheng, Nan Xue, Huijuan Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Estimating 3D full-body avatars from AR/VR devices is essential for creating immersive experiences in AR/VR applications. This task is challenging due to the limited input from Head Mounted Devices which capture only sparse observations from the head and hands. Predicting the full-body avatars particularly the lower body from these sparse observations presents significant difficulties. In this paper we are inspired by the inherent property of the kinematic tree defined in the Skinned Multi-Person Linear (SMPL) model where the upper body and lower body share only one common ancestor node bringing the potential of decoupled reconstruction. We propose a stratified approach to decouple the conventional full-body avatar reconstruction pipeline into two stages with the reconstruction of the upper body first and a subsequent reconstruction of the lower body conditioned on the previous stage. To implement this straightforward idea we leverage the latent diffusion model as a powerful probabilistic generator and train it to follow the latent distribution of decoupled motions explored by a VQ-VAE encoder-decoder model. Extensive experiments on AMASS mocap dataset demonstrate our state-of-the-art performance in the reconstruction of full-body motions.
Learning to Segment Referred Objects from Narrated Egocentric Videos
Authors: Yuhan Shen, Huiyu Wang, Xitong Yang, Matt Feiszli, Ehsan Elhamifar, Lorenzo Torresani, Effrosyni Mavroudi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Egocentric videos provide a first-person perspective of the wearer's activities involving simultaneous interactions with multiple objects. In this work we propose the task of weakly-supervised Narration-based Video Object Segmentation (NVOS). Given an egocentric video clip and a narration of the wearer's activities our aim is to segment object instances mentioned in the narration without using any spatial annotations during training. Existing weakly-supervised video object grounding methods typically yield bounding boxes for referred objects. In contrast we propose ROSA a weakly-supervised pixel-level grounding framework learning alignments between referred objects and segmentation mask proposals. Our model harnesses vision-language models pre-trained on image-text pairs to embed region masks and object phrases. During training we combine (a) a video-narration contrastive loss that implicitly supervises the alignment between regions and phrases and (b) a region-phrase contrastive loss based on inferred latent alignments. To address the lack of annotated NVOS datasets in egocentric videos we create a new evaluation benchmark VISOR-NVOS leveraging existing annotations of segmentation masks from VISOR alongside 14.6k newly-collected object-based video clip narrations. Our approach achieves state-of-the-art zero-shot pixel-level grounding performance compared to strong baselines under similar supervision. Additionally we demonstrate generalization capabilities for zero-shot video object grounding on YouCook2 a third-person instructional video dataset.
Rewrite the Stars
Authors: Xu Ma, Xiyang Dai, Yue Bai, Yizhou Wang, Yun Fu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design. While intuitive explanations abound the foundational rationale behind its application remains largely unexplored. Our study attempts to reveal the star operation's ability of mapping inputs into high-dimensional non-linear feature spaces--akin to kernel tricks--without widening the network. We further introduce StarNet a simple yet powerful prototype demonstrating impressive performance and low latency under compact network structure and efficient budget. Like stars in the sky the star operation appears unremarkable but holds a vast universe of potential. Our work encourages further exploration across tasks with codes available at https://github.com/ma-xu/Rewrite-the-Stars.
Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images
Authors: Chaoqin Huang, Aofan Jiang, Jinghao Feng, Ya Zhang, Xinchao Wang, Yanfeng Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in large-scale visual-language pre-trained models have led to significant progress in zero-/few-shot anomaly detection within natural image domains. However the substantial domain divergence between natural and medical images limits the effectiveness of these methodologies in medical anomaly detection. This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder enabling a stepwise enhancement of visual features across different levels. This multi-level adaptation is guided by multi-level pixel-wise visual-language feature alignment loss functions which recalibrate the model's focus from object semantics in natural imagery to anomaly identification in medical images. The adapted features exhibit improved generalization across various medical data types even in zero-shot scenarios where the model encounters unseen medical modalities and anatomical regions during training. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models with an average AUC improvement of 6.24% and 7.33% for anomaly classification 2.03% and 2.37% for anomaly segmentation under the zero-shot and few-shot settings respectively. Source code is available at: https://github.com/MediaBrain-SJTU/MVFA-AD
AV-RIR: Audio-Visual Room Impulse Response Estimation
Authors: Anton Ratnarajah, Sreyan Ghosh, Sonal Kumar, Purva Chiniya, Dinesh Manocha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
Authors: Weihuang Liu, Xi Shen, Haolun Li, Xiuli Bi, Bo Liu, Chi-Man Pun, Xiaodong Cun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model on large-scale video datasets which struggle to generalize to unseen videos. In this work we introduce a test-time training (TTT) strategy to address the problem. Our key insight is to enforce the model to predict consistent depth during the TTT process. In detail we first train a single network to perform both segmentation and depth prediction tasks. This can be effectively learned with our specifically designed depth modulation layer. Then for the TTT process the model is updated by predicting consistent depth maps for the same frame under different data augmentations. In addition we explore different TTT weight update strategies. Our empirical results suggest that the momentum-based weight initialization and looping-based training scheme lead to more stable improvements. Experiments show that the proposed method achieves clear improvements on ZSVOS. Our proposed video TTT strategy provides significant superiority over state-of-the-art TTT methods. Our code is available at: https://nifangbaage.github.io/DATTT/.
Dual-Consistency Model Inversion for Non-Exemplar Class Incremental Learning
Authors: Zihuan Qiu, Yi Xu, Fanman Meng, Hongliang Li, Linfeng Xu, Qingbo Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Non-exemplar class incremental learning (NECIL) aims to continuously assimilate new knowledge without forgetting previously acquired ones when historical data are unavailable. One of the generative NECIL methods is to invert the images of old classes for joint training. However these synthetic images suffer significant domain shifts compared with real data hampering the recognition of old classes. In this paper we present a novel method termed Dual-Consistency Model Inversion (DCMI) to generate better synthetic samples of old classes through two pivotal consistency alignments: (1) the semantic consistency between the synthetic images and the corresponding prototypes and (2) domain consistency between synthetic and real images of new classes. Besides we introduce Prototypical Routing (PR) to provide task-prior information and generate unbiased and accurate predictions. Our comprehensive experiments across diverse datasets consistently showcase the superiority of our method over previous state-of-the-art approaches.
RMem: Restricted Memory Banks Improve Video Object Segmentation
Authors: Junbao Zhou, Ziqi Pang, Yu-Xiong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With recent video object segmentation (VOS) benchmarks evolving to challenging scenarios we revisit a simple but overlooked strategy: restricting the size of memory banks. This diverges from the prevalent practice of expanding memory banks to accommodate extensive historical information. Our specially designed "memory deciphering" study offers a pivotal insight underpinning such a strategy: expanding memory banks while seemingly beneficial actually increases the difficulty for VOS modules to decode relevant features due to the confusion from redundant information. By restricting memory banks to a limited number of essential frames we achieve a notable improvement in VOS accuracy. This process balances the importance and freshness of frames to maintain an informative memory bank within a bounded capacity. Additionally restricted memory banks reduce the training-inference discrepancy in memory lengths compared with continuous expansion. This fosters new opportunities in temporal reasoning and enables us to introduce the previously overlooked "temporal positional embedding." Finally our insights are embodied in "RMem" ("R" for restricted) a simple yet effective VOS modification that excels at challenging VOS scenarios and establishes new state of the art for object state changes (VOST dataset) and long videos (the Long Videos dataset). Our codes are available at https://github.com/Restricted-Memory/RMemand our demo can be watched on https://youtu.be/V3tCFQsJrrM.
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers
Authors: Sheng Yang, Jiawang Bai, Kuofeng Gao, Yong Yang, Yiming Li, Shu-Tao Xia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Given the power of vision transformers a new learning paradigm pre-training and then prompting makes it more efficient and effective to address downstream visual recognition tasks. In this paper we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically an extra prompt token called the switch token in this work can turn the backdoor mode on i.e. converting a benign model into a backdoored one. Once under the backdoor mode a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API since the malicious behavior can not be activated and detected under the benign mode thus making the attack very stealthy. To attack a pre-trained model our proposed attack named SWARM learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides we utilize the cross-mode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i.e. achieving 95%+ attack success rate and also being hard to be detected and removed. Our code is available at https://github.com/20000yshust/SWARM.
PairDETR : Joint Detection and Association of Human Bodies and Faces
Authors: Ammar Ali, Georgii Gaikov, Denis Rybalchenko, Alexander Chigorin, Ivan Laptev, Sergey Zagoruyko
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Image and video analysis requires not only accurate object but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object detectors followed by non-differentiable post-processing techniques. Recently introduced detection transformers (DETR) perform end-to-end object detection based on a bipartite matching loss. Such methods however lack the ability to jointly detect objects and resolve object associations. In this paper we build on the DETR approach and extend it to the joint detection of objects and their relationships by introducing an approximated bipartite matching. While our method can generalize to an arbitrary number of objects we here focus on the modeling of object pairs and their relations. In particular we apply our method PairDETR to the problem of detecting human bodies and faces and associating them for the same person. Our approach not only eliminates the need for hand-designed post-processing but also achieves excellent results for body-face associations. We evaluate PairDETR on the challenging CrowdHuman and CityPersons datasets and demonstrate a large improvement over the state of the art. Our training code and pre-trained models are available online.
PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization
Authors: Xu Peng, Junwei Zhu, Boyuan Jiang, Ying Tai, Donghao Luo, Jiangning Zhang, Wei Lin, Taisong Jin, Chengjie Wang, Rongrong Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in personalized image generation using diffusion models have been noteworthy. However existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment limiting practical usability. Moreover these methods often grapple with identity distortion and limited expression diversity. In light of these challenges we propose PortraitBooth an innovative approach designed for high efficiency robust identity preservation and expression-editable text-to-image generation without the need for fine-tuning. PortraitBooth leverages subject embeddings from a face recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios.
Learn from View Correlation: An Anchor Enhancement Strategy for Multi-view Clustering
Authors: Suyuan Liu, Ke Liang, Zhibin Dong, Siwei Wang, Xihong Yang, Sihang Zhou, En Zhu, Xinwang Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In recent years anchor-based methods have achieved promising progress in multi-view clustering. The performances of these methods are significantly affected by the quality of the anchors. However the anchors generated by previous works solely rely on single-view information ignoring the correlation among different views. In particular we observe that similar patterns are more likely to exist between similar views so such correlation information can be leveraged to enhance the quality of the anchors which is also omitted. To this end we propose a novel plug-and-play anchor enhancement strategy through view correlation for multi-view clustering. Specifically we construct a view graph based on aligned initial anchor graphs to explore inter-view correlations. By learning from view correlation we enhance the anchors of the current view using the relationships between anchors and samples on neighboring views thereby narrowing the spatial distribution of anchors on similar views. Experimental results on seven datasets demonstrate the superiority of our proposed method over other existing methods. Furthermore extensive comparative experiments validate the effectiveness of the proposed anchor enhancement module when applied to various anchor-based methods.
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
Authors: Weizhao He, Yang Zhang, Wei Zhuo, Linlin Shen, Jiaqi Yang, Songhe Deng, Liang Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction
Authors: Junuk Cha, Jihyeon Kim, Jae Shin Yoon, Seungryul Baek
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper introduces the first text-guided work for generating the sequence of hand-object interaction in 3D. The main challenge arises from the lack of labeled data where existing ground-truth datasets are nowhere near generalizable in interaction type and object category which inhibits the modeling of diverse 3D hand-object interaction with the correct physical implication (e.g. contacts and semantics) from text prompts. To address this challenge we propose to decompose the interaction generation task into two subtasks: hand-object contact generation; and hand-object motion generation. For contact generation a VAE-based network takes as input a text and an object mesh and generates the probability of contacts between the surfaces of hands and the object during the interaction. The network learns a variety of local geometry structure of diverse objects that is independent of the objects' category and thus it is applicable to general objects. For motion generation a Transformer-based diffusion model utilizes this 3D contact map as a strong prior for generating physically plausible hand-object motion as a function of text prompts by learning from the augmented labeled dataset; where we annotate text labels from many existing 3D hand and object motion data. Finally we further introduce a hand refiner module that minimizes the distance between the object surface and hand joints to improve the temporal stability of the object-hand contacts and to suppress the penetration artifacts. In the experiments we demonstrate that our method can generate more realistic and diverse interactions compared to other baseline methods. We also show that our method is applicable to unseen objects. We will release our model and newly labeled data as a strong foundation for future research. Codes and data are available in: https://github.com/JunukCha/Text2HOI.
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Authors: Hongjie Wang, Bhishma Dedhia, Niraj K. Jha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployment on various Transformer backbones. However most token pruning methods require computationally expensive fine-tuning which is undesirable in many edge deployment cases. In this work we propose Zero-TPrune the first zero-shot method that considers both the importance and similarity of tokens in performing token pruning. It leverages the attention graph of pre-trained Transformer models to produce an importance distribution for tokens via our proposed Weighted Page Rank (WPR) algorithm. This distribution further guides token partitioning for efficient similarity-based pruning. Due to the elimination of the fine-tuning overhead Zero-TPrune can prune large models at negligible computational cost switch between different pruning configurations at no computational cost and perform hyperparameter tuning efficiently. We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet. Without any fine-tuning Zero-TPrune reduces the FLOPs cost of DeiT-S by 34.7% and improves its throughput by 45.3% with only 0.4% accuracy loss. Compared with state-of-the-art pruning methods that require fine-tuning Zero-TPrune not only eliminates the need for fine-tuning after pruning but also does so with only 0.1% accuracy loss. Compared with state-of-the-art fine-tuning-free pruning methods Zero-TPrune reduces accuracy loss by up to 49% with the same or higher throughput.
Enhancing Visual Continual Learning with Language-Guided Supervision
Authors: Bolin Ni, Hongbo Zhao, Chenghao Zhang, Ke Hu, Gaofeng Meng, Zhaoxiang Zhang, Shiming Xiang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures replay data regularization etc. However the category name of each class is largely neglected. Existing methods commonly utilize the one-hot labels and randomly initialize the classifier head. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. In this paper we revisit the role of the classifier head within the CL paradigm and replace the classifier with semantic knowledge from pretrained language models (PLMs). Specifically we use PLMs to generate semantic targets for each class which are frozen and serve as supervision signals during training. Such targets fully consider the semantic correlation between all classes across tasks. Empirical studies show that our approach mitigates forgetting by alleviating representation drifting and facilitating knowledge transfer across tasks. The proposed method is simple to implement and can seamlessly be plugged into existing methods with negligible adjustments. Extensive experiments based on eleven mainstream baselines demonstrate the effectiveness and generalizability of our approach to various protocols. For example under the class-incremental learning setting on ImageNet-100 our method significantly improves the Top-1 accuracy by 3.2% to 6.1% while reducing the forgetting rate by 2.6% to 13.1%.
MACE: Mass Concept Erasure in Diffusion Models
Authors: Shilin Lu, Zilan Wang, Leyang Li, Yanzhu Liu, Adams Wai-Kin Kong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper we introduce MACE a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning collectively eliminating the information of undesirable concepts. Furthermore MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure celebrity erasure explicit content erasure and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.
DIBS: Enhancing Dense Video Captioning with Unlabeled Videos via Pseudo Boundary Enrichment and Online Refinement
Authors: Hao Wu, Huabin Liu, Yu Qiao, Xiao Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Dive Into the Boundaries (DIBS) a novel pretraining framework for dense video captioning (DVC) that elaborates on improving the quality of the generated event captions and their associated pseudo event boundaries from unlabeled videos. By leveraging the capabilities of diverse large language models (LLMs) we generate rich DVC-oriented caption candidates and optimize the corresponding pseudo boundaries under several meticulously designed objectives considering diversity event-centricity temporal ordering and coherence. Moreover we further introduce a novel online boundary refinement strategy that iteratively improves the quality of pseudo boundaries during training. Comprehensive experiments have been conducted to examine the effectiveness of the proposed technique components. By leveraging a substantial amount of unlabeled video data such as HowTo100M we achieve a remarkable advancement on standard DVC datasets like YouCook2 and ActivityNet. We outperform the previous state-of-the-art Vid2Seq across a majority of metrics achieving this with just 0.4% of the unlabeled video data used for pre-training by Vid2Seq.
PeLK: Parameter-efficient Large Kernel ConvNets with Peripheral Convolution
Authors: Honghao Chen, Xiangxiang Chu, Yongjian Ren, Xin Zhao, Kaiqi Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently some large kernel convnets strike back with appealing performance and efficiency. However given the square complexity of convolution scaling up kernels can bring about an enormous amount of parameters and the proliferated parameters can induce severe optimization problem. Due to these issues current CNNs compromise to scale up to 51x51 in the form of stripe convolution (i.e. 51x5+5x51) and start to saturate as the kernel size continues growing. In this paper we delve into addressing these vital issues and explore whether we can continue scaling up kernels for more performance gains. Inspired by human vision we propose a human-like peripheral convolution that efficiently reduces over 90% parameter count of dense grid convolution through parameter sharing and manage to scale up kernel size to extremely large. Our peripheral convolution behaves highly similar to human reducing the complexity of convolution from O(K^2) to O(logK) without backfiring performance. Built on this we propose Parameter-efficient Large Kernel Network (PeLK). Our PeLK outperforms modern vision Transformers and ConvNet architectures like Swin ConvNeXt RepLKNet and SLaK on various vision tasks including ImageNet classification semantic segmentation on ADE20K and object detection on MS COCO. For the first time we successfully scale up the kernel size of CNNs to an unprecedented 101x101 and demonstrate consistent improvements.
AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation
Authors: Qingping Sun, Yanjun Wang, Ailing Zeng, Wanqi Yin, Chen Wei, Wenjia Wang, Haiyi Mei, Chi-Sing Leung, Ziwei Liu, Lei Yang, Zhongang Cai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body hand and expression estimation. Most existing methods have tackled this task in a two-stage manner first detecting the human body part with an off-the-shelf detection model and then inferring the different human body parts individually. Despite the impressive results achieved these methods suffer from 1) loss of valuable contextual information via cropping 2) introducing distractions and 3) lacking inter-association among different persons and body parts inevitably causing performance degradation especially for crowded scenes. To address these issues we introduce a novel all-in-one-stage framework AiOS for multiple expressive human pose and shape recovery without an additional human detection step. Specifically our method is built upon DETR which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection. We devise the decoder tokens and extend them to our task. Specifically we first employ a human token to probe a human location in the image and encode global features for each instance which provides a coarse location for the later transformer block. Then we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature which collaborates with the global feature to regress the whole-body mesh. This straightforward but effective model outperforms previous state-of-the-art methods by a 9 reduction in NMVE on AGORA a 30 reduction in PVE on EHF a 10 reduction in PVE on ARCTIC and a 3 reduction in PVE on EgoBody.
SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge
Authors: Andong Wang, Bo Wu, Sunli Chen, Zhenfang Chen, Haotian Guan, Wei-Ning Lee, Li Erran Li, Chuang Gan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Reasoning from visual dynamics scenes has many real world applications. However existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reasoning evaluations specifically within dynamic open-world and structured context knowledge. We propose a new benchmark (SOK-Bench) consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. To create such a dataset we propose an automatic and scalable generation method to generate question-answer pairs knowledge graphs and rationales by instructing the combinations of LLMs and MLLMs. Concretely we first extract observable situated entities relations and processes from videos for situated knowledge and then extend to open-world knowledge beyond the visible content. The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense we generate associated question-answer pairs and reasoning processes finally followed by manual reviews for quality assurance. We evaluated recent mainstream large vision language models on the benchmark and found several insightful conclusions. For more information please refer to our benchmark at www.bobbywu.com/SOKBench.
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Authors: Jialin Li, Qiang Nie, Weifu Fu, Yuhuan Lin, Guangpin Tao, Yong Liu, Chengjie Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep learning models particularly those based on transformers often employ numerous stacked structures which possess identical architectures and perform similar functions. While effective this stacking paradigm leads to a substantial increase in the number of parameters pos- ing challenges for practical applications. In today's land- scape of increasingly large models stacking depth can even reach dozens further exacerbating this issue. To miti- gate this problem we introduce LORS (LOw-rank Residual Structure). LORS allows stacked modules to share the majority of parameters requiring a much smaller num- ber of unique ones per module to match or even surpass the performance of using entirely distinct ones thereby significantly reducing parameter usage. We validate our method by applying it to the stacked decoders of a query- based object detector and conduct extensive experiments on the widely used MS COCO dataset. Experimental re- sults demonstrate the effectiveness of our method as even with a 70% reduction in the parameters of the decoder our method still enables the model to achieve comparable or even better performance than its original.
Design2Cloth: 3D Cloth Generation from 2D Masks
Authors: Jiali Zheng, Rolandos Alexandros Potamias, Stefanos Zafeiriou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In recent years there has been a significant shift in the field of digital avatar research towards modeling animating and reconstructing clothed human representations as a key step towards creating realistic avatars. However current 3D cloth generation methods are garment specific or trained completely on synthetic data hence lacking fine details and realism. In this work we make a step towards automatic realistic garment design and propose Design2Cloth a high fidelity 3D generative model trained on a real world dataset from more than 2000 subject scans. To provide vital contribution to the fashion industry we developed a user-friendly adversarial model capable of generating diverse and detailed clothes simply by drawing a 2D cloth mask. Under a series of both qualitative and quantitative experiments we showcase that Design2Cloth outperforms current state-of-the-art cloth generative models by a large margin. In addition to the generative properties of our network we showcase that the proposed method can be used to achieve high quality reconstructions from single in-the-wild images and 3D scans. Dataset code and pre-trained model will become publicly available.
Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer
Authors: Zhen Zhao, Jingqun Tang, Chunhui Lin, Binghong Wu, Can Huang, Hao Liu, Xin Tan, Zhizhong Zhang, Yuan Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Scene text recognition (STR) in the wild frequently encounters challenges when coping with domain variations font diversity shape deformations etc. A straightforward solution is performing model fine-tuning tailored to a specific scenario but it is computationally intensive and requires multiple model copies for various scenarios. Recent studies indicate that large language models (LLMs) can learn from a few demonstration examples in a training-free manner termed "In-Context Learning" (ICL). Nevertheless applying LLMs as a text recognizer is unacceptably resource-consuming. Moreover our pilot experiments on LLMs show that ICL fails in STR mainly attributed to the insufficient incorporation of contextual information from diverse samples in the training stage. To this end we introduce E2STR a STR model trained with context-rich scene text sequences where the sequences are generated via our proposed in-context training strategy. E2STR demonstrates that a regular-sized model is sufficient to achieve effective ICL capabilities in STR. Extensive experiments show that E2STR exhibits remarkable training-free adaptation in various scenarios and outperforms even the fine-tuned state-of-the-art approaches on public benchmarks. The code is released at https://github.com/bytedance/E2STR.
Amodal Completion via Progressive Mixed Context Diffusion
Authors: Katherine Xu, Lingzhi Zhang, Jianbo Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Our brain can effortlessly recognize objects even when partially hidden from view. Seeing the visible of the hidden is called amodal completion; however this task remains a challenge for generative AI despite rapid progress. We propose to sidestep many of the difficulties of existing approaches which typically involve a two-step process of predicting amodal masks and then generating pixels. Our method involves thinking outside the box literally! We go outside the object bounding box to use its context to guide a pre-trained diffusion inpainting model and then progressively grow the occluded object and trim the extra background. We overcome two technical challenges: 1) how to be free of unwanted co-occurrence bias which tends to regenerate similar occluders and 2) how to judge if an amodal completion has succeeded. Our amodal completion method exhibits improved photorealistic completion results compared to existing approaches in numerous successful completion cases. And the best part? It doesn't require any special training or fine-tuning of models. Project page and code: https://k8xu.github.io/amodal/
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning
Authors: Zichen Miao, Jiang Wang, Ze Wang, Zhengyuan Yang, Lijuan Wang, Qiang Qiu, Zicheng Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models have demonstrated unprecedented capabilities in image generation. Yet they incorporate and amplify the data bias (e.g. gender age) from the original training set limiting the diversity of generated images. In this paper we propose a diversity-oriented fine-tuning method using reinforcement learning (RL) for diffusion models under the guidance of an image-set-based reward function. Specifically the proposed reward function denoted as Diversity Reward utilizes a set of generated images to evaluate the coverage of the current generative distribution w.r.t. the reference distribution represented by a set of unbiased images. Built on top of the probabilistic method of distribution discrepancy estimation Diversity Reward can measure the relative distribution gap with a small set of images efficiently. We further formulate the diffusion process as a multi-step decision-making problem (MDP) and apply policy gradient methods to fine-tune diffusion models by maximizing the Diversity Reward. The proposed rewards are validated on a post-sampling selection task where a subset of the most diverse images are selected based on Diversity Reward values. We also show the effectiveness of our RL fine-tuning framework on enhancing the diversity of image generation with different types of diffusion models including class-conditional models and text-conditional models e.g. StableDiffusion.
Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features
Authors: Niladri Shekhar Dutt, Sanjeev Muralikrishnan, Niloy J. Mitra
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Diff3F as a simple robust and class-agnostic feature descriptor that can be computed for untextured input shapes (meshes or point clouds). Our method distills diffusion features from image foundational models onto input shapes. Specifically we use the input shapes to produce depth and normal maps as guidance for conditional image synthesis. In the process we produce (diffusion) features in 2D that we subsequently lift and aggregate on the original surface. Our key observation is that even if the conditional image generations obtained from multi-view rendering of the input shapes are inconsistent the associated image features are robust and hence can be directly aggregated across views. This produces semantic features on the input shapes without requiring additional data or training. We perform extensive experiments on multiple benchmarks (SHREC'19 SHREC'20 FAUST and TOSCA) and demonstrate that our features being semantic instead of geometric produce reliable correspondence across both isometric and non-isometrically related shape families. Code is available via the project webpage at https://diff3f.github.io/
LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation
Authors: Ke Guo, Zhenwei Miao, Wei Jing, Weiwei Liu, Weizi Li, Dayang Hao, Jia Pan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. However creating a realistic simulator that accurately replicates human driving behaviors in various traffic conditions presents significant challenges. Traditional simulators relying on heuristic models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. Due to the covariate shift issue existing imitation learning-based simulators often fail to generate stable long-term simulations. In this paper we propose a novel approach called learner-aware supervised imitation learning to address the covariate shift problem in multi-agent imitation learning. By leveraging a variational autoencoder simultaneously modeling the expert and learner state distribution our approach augments expert states such that the augmented state is aware of learner state distribution. Our method applied to urban traffic simulation demonstrates significant improvements over existing state-of-the-art baselines in both short-term microscopic and long-term macroscopic realism when evaluated on the real-world dataset pNEUMA.
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space
Authors: K Naveen Kumar, Reshmi Mitra, C Krishna Mohan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Federated Learning (FL) facilitates clients to collaborate on training a shared machine learning model without exposing individual private data. Nonetheless FL remains susceptible to utility and privacy attacks notably evasion data poisoning and model inversion attacks compromising the system's efficiency and data privacy. Existing FL defenses are often specialized to a particular single attack lacking generality and a comprehensive defender's perspective. To address these challenges we introduce Federated Cryptography Defense (FCD) a unified single framework aligning with the defender's perspective. FCD employs row-wise transposition cipher based data encryption with a secret key to counter both evasion black-box data poisoning and model inversion attacks. The crux of FCD lies in transferring the entire learning process into an encrypted data space and using a novel distillation loss guided by the Kullback-Leibler (KL) divergence. This measure compares the probability distributions of the local pretrained teacher model's predictions on normal data and the local student model's predictions on the same data in FCD's encrypted form. By working within this encrypted space FCD eliminates the need for decryption at the server resulting in reduced computational complexity. We demonstrate the practical feasibility of FCD and apply it to defend against evasion utility attack on benchmark datasets (GTSRB KBTS CIFAR10 and EMNIST). We further extend FCD for defending against model inversion attack in split FL on the CIFAR100 dataset. Our experiments across the diverse attack and FL settings demonstrate practical feasibility and robustness against utility evasion (impact >30) and privacy attacks (MSE >73) compared to the second best method.