mtybilly/Instruct2DS huggingface dataset:
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
Authors: Heeseong Shin, Sunghwan Hong, Anurag Arnab, Paul Hongsuck Seo, Seungryong Kim Seokju Cho
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work we introduce a novel cost-based approach to adapt vision-language foundation models notably CLIP for the intricate task of semantic segmentation. Through aggregating the cosine similarity score i.e. the cost volume between image and text embeddings our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders addressing the challenges faced by existing methods in handling unseen classes. Building upon this we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore we examine various methods for efficiently fine-tuning CLIP.
Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation
Authors: Tianwei Lin Qin Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently diffusion-based methods like InstructPix2Pix (IP2P) have achieved effective instruction-based image editing requiring only natural language instructions from the user. However these methods often inadvertently alter unintended areas and struggle with multi-instruction editing resulting in compromised outcomes. To address these issues we introduce the Focus on Your Instruction (FoI) a method designed to ensure precise and harmonious editing across multiple instructions without extra training or test-time optimization. In the FoI we primarily emphasize two aspects: (1) precisely extracting regions of interest for each instruction and (2) guiding the denoising process to concentrate within these regions of interest. For the first objective we identify the implicit grounding capability of IP2P from the cross-attention between instruction and image then develop an effective mask extraction method.
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Authors: Jiazheng Xing, Yijie Qian, Yaowei Guo, Shuo Xin, Junhao Chen, Kai Tang, Mengmeng Wang, Zhengkai Jiang, Liang Liu, Yong Liu Xiaojun Hou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. However the modality gap limits pre-trained knowledge recall and the dominance of the RGB modality persists preventing the full utilization of information from other modalities. To address these issues we propose a novel symmetric multimodal tracking framework called SDSTrack. We introduce lightweight adaptation for efficient fine-tuning which directly transfers the feature extraction ability from RGB to other domains with a small number of trainable parameters and integrates multimodal features in a balanced symmetric manner. Furthermore we design a complementary masked patch distillation strategy to enhance the robustness of trackers in complex environments such as extreme weather poor imaging and sensor failure. Extensive experiments demonstrate that SDSTrack outperforms state-of-the-art methods in various multimodal tracking scenarios including RGB+Depth RGB+Thermal and RGB+Event tracking and exhibits impressive results in extreme conditions. Our source code is available at : https://github.com/hoqolo/SDSTrack.
MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes
Authors: Chien-Yi Wang, Wei-Chen Chiu Bor-Shiun Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. Although being effective in bridging the semantic gap between a model's latent space and human interpretation these explanation methods only partially reveal the model's decision-making process. The outcome is typically limited to high-level semantics derived from the last feature map. We argue that the explanations lacking insights into the decision processes at low and mid-level features are neither fully faithful nor useful. Addressing this gap we introduce the Multi-Level Concept Prototypes Classifier (MCPNet) an inherently interpretable model. MCPNet autonomously learns meaningful concept prototypes across multiple feature map levels using Centered Kernel Alignment (CKA) loss and an energy-based weighted PCA mechanism and it does so without reliance on predefined concept labels. Further we propose a novel classifier paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss. Our experiments reveal that our proposed MCPNet while being adaptable to various model architectures offers comprehensive multi-level explanations while maintaining classification accuracy. Additionally its concept distribution-based classification approach shows improved generalization capabilities in few-shot classification scenarios.
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment
Authors: Christopher Thomas Alvi Md Ishmam
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats such as backdooring and poisoning attacks. In this paper we propose a method for mitigating such attacks on contrastively trained vision-language models. Our approach leverages external knowledge extracted from a language model to prevent models from learning correlations between image regions which lack strong alignment with external knowledge. We do this by imposing constraints to enforce that attention paid by the model to visual regions is proportional to the alignment of those regions with external knowledge. We conduct extensive experiments using a variety of recent backdooring and poisoning attacks on multiple datasets and architectures. Our results clearly demonstrate that our proposed approach is highly effective at defending against such attacks across multiple settings while maintaining model utility and without requiring any changes at inference time.
AvatarGPT: All-in-One Framework for Motion Understanding Planning Generation and Beyond
Authors: Yu Wan, Baoyuan Wang Zixiang Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large Language Models(LLMs) have shown remarkable emergent abilities in unifying almost all (if not every) NLP tasks. In the human motion-related realm however researchers still develop siloed models for each task. Inspired by InstuctGPT[??] and the generalist concept behind Gato [??] we introduce AvatarGPT an All-in-One framework for motion understanding planning generations as well as other tasks such as motion in-between synthesis. AvatarGPT treats each task as one type of instruction fine-tuned on the shared LLM. All the tasks are seamlessly interconnected with language as the universal interface constituting a closed-loop within the framework. To achieve this human motion sequences are first encoded as discrete tokens which serve as the extended vocabulary of LLM. Then an unsupervised pipeline to generate natural language descriptions of human action sequences from in-the-wild videos is developed. Finally all tasks are jointly trained. Extensive experiments show that AvatarGPT achieves SOTA on low-level tasks and promising results on high-level tasks demonstrating the effectiveness of our proposed All-in-One framework. Moreover for the first time AvatarGPT enables a principled approach by iterative traversal of the tasks within the closed-loop for unlimited long-motion synthesis.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Authors: Yao Zhao, Shikui Wei, Guanghua Gu, Ping Liu, Yunchao Wei Chuangchuang Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently the proliferation of highly realistic synthetic images facilitated through a variety of GANs and Diffusions has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can beyond frequency-based artifacts produce generalized forgery artifacts. In particular the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance showcasing a remarkable 12.8% improvement over existing methods. The code is available at https://github.com/chuangchuangtan/NPR-DeepfakeDetection.
Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model
Authors: Qiaochu Huang, Zhensong Zhang, Zhiwei Lin, Zhiyong Wu, Sicheng Yang, Minglei Li, Zhiyi Chen, Songcen Xu, Xiaofei Wu Xu He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Co-speech gestures if presented in the lively form of videos can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons resulting in the omission of appearance information we focus on the direct generation of audio-driven co-speech gesture videos in this work. There are two main challenges: 1) A suitable motion feature is needed to describe complex human movements with crucial appearance information. 2) Gestures and speech exhibit inherent dependencies and should be temporally aligned even of arbitrary length. To solve these problems we present a novel motion-decoupled framework to generate co-speech gesture videos. Specifically we first introduce a well-designed nonlinear TPS transformation to obtain latent motion features preserving essential appearance information. Then a transformer-based diffusion model is proposed to learn the temporal correlation between gestures and speech and performs generation in the latent motion space followed by an optimal motion selection module to produce long-term coherent and consistent gesture videos. For better visual perception we further design a refinement network focusing on missing details of certain areas. Extensive experimental results show that our proposed framework significantly outperforms existing approaches in both motion and video-related evaluations. Our code demos and more resources are available at https://github.com/thuhcsi/S2G-MDDiffusion.
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution
Authors: Chenyi Zhuang, Pan Gao, Jie Qin Qingguo Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing Blind image Super-Resolution (BSR) methods focus on estimating either kernel or degradation information but have long overlooked the essential content details. In this paper we propose a novel BSR approach Content-aware Degradation-driven Transformer (CDFormer) to capture both degradation and content representations. However low-resolution images cannot provide enough content details and thus we introduce a diffusion-based module CDFormer_ diff  to first learn Content Degradation Prior (CDP) in both low- and high-resolution images and then approximate the real distribution given only low-resolution information. Moreover we apply an adaptive SR network CDFormer_ SR  that effectively utilizes CDP to refine features. Compared to previous diffusion-based SR methods we treat the diffusion model as an estimator that can overcome the limitations of expensive sampling time and excessive diversity. Experiments show that CDFormer can outperform existing methods establishing a new state-of-the-art performance on various benchmarks under blind settings. Codes and models will be available at https://github.com/I2-Multimedia-Lab/CDFormer.
HumanRef: Single Image to 3D Human Generation via Reference-Guided Diffusion
Authors: Xiaoyu Li, Qi Zhang, Yanpei Cao, Ying Shan, Jing Liao Jingbo Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generating a 3D human model from a single reference image is challenging because it requires inferring textures and geometries in invisible views while maintaining consistency with the reference image. Previous methods utilizing 3D generative models are limited by the availability of 3D training data. Optimization-based methods that lift text-to-image diffusion models to 3D generation often fail to preserve the texture details of the reference image resulting in inconsistent appearances in different views. In this paper we propose HumanRef a 3D human generation framework from a single-view input. To ensure the generated 3D model is photorealistic and consistent with the input image HumanRef introduces a novel method called reference-guided score distillation sampling (Ref-SDS) which effectively incorporates image guidance into the generation process. Furthermore we introduce region-aware attention to Ref-SDS ensuring accurate correspondence between different body regions. Experimental results demonstrate that HumanRef outperforms state-of-the-art methods in generating 3D clothed humans with fine geometry photorealistic textures and view-consistent appearances. Code and model are available at https://eckertzhang.github.io/HumanRef.github.io/.
GlitchBench: Can Large Multimodal Models Detect Video Game Glitches?
Authors: Tianjun Feng, Cor-Paul Bezemer, Anh Nguyen Mohammad Reza Taesiri
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large multimodal models (LMMs) have evolved from large language models (LLMs) to integrate multiple input modalities such as visual inputs. This integration augments the capacity of LLMs for tasks requiring visual comprehension and reasoning. However the extent and limitations of their enhanced abilities are not fully understood especially when it comes to real-world tasks. To address this gap we introduce GlitchBench a novel benchmark derived from video game quality assurance tasks to test and evaluate the reasoning capabilities of LMMs. Our benchmark is curated from a variety of unusual and glitched scenarios from video games and aims to challenge both the visual and linguistic reasoning powers of LMMs in detecting and interpreting out-of-the-ordinary events. We evaluate multiple state-of-the-art LMMs and we show that GlitchBench presents a new challenge for these models. Code and data are available at: https://glitchbench.github.io/
Rethinking Interactive Image Segmentation with Low Latency High Quality and Diverse Prompts
Authors: Jaemin Cho, Mohit Bansal, Marc Niethammer Qin Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation with diverse prompts remain challenging for existing specialist and generalist models. Specialist models with their limited prompts and task-specific designs experience high latency because the image must be recomputed every time the prompt is updated due to the joint encoding of image and visual prompts. Generalist models exemplified by the Segment Anything Model (SAM) have recently excelled in prompt diversity and efficiency lifting image segmentation to the foundation model era. However for high-quality segmentations SAM still lags behind state-of-the-art specialist models despite SAM being trained with x100 more segmentation masks. In this work we delve deep into the architectural differences between the two types of models. We observe that dense representation and fusion of visual prompts are the key design choices contributing to the high segmentation quality of specialist models. In light of this we reintroduce this dense design into the generalist models to facilitate the development of generalist models with high segmentation quality. To densely represent diverse visual prompts we propose to use a dense map to capture five types: clicks boxes polygons scribbles and masks. Thus we propose SegNext a next-generation interactive segmentation approach offering low latency high quality and diverse prompt support. Our method outperforms current state-of-the-art methods on HQSeg-44K and DAVIS quantitatively and qualitatively.
ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers
Authors: Svetlana Orlova, Daan de Geus, Gijs Dubbelman Narges Norouzi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This work presents Adaptive Local-then-Global Merging (ALGM) a token reduction method for semantic segmentation networks that use plain Vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer it merges similar tokens within a small local window and (2) halfway through the network it merges similar tokens across the entire image. This is motivated by an analysis in which we found that in those situations tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations we show that ALGM not only significantly improves the throughput by up to 100% but can also enhance the mean IoU by up to +1.1 thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover our approach is adaptive during inference meaning that the same model can be used for optimal efficiency or accuracy depending on the application. Code is available at https://tue-mps.github.io/ALGM.
DITTO: Dual and Integrated Latent Topologies for Implicit 3D Reconstruction
Authors: Kyungdon Joo Jaehyeok Shim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a novel concept of dual and integrated latent topologies (DITTO in short) for implicit 3D reconstruction from noisy and sparse point clouds. Most existing methods predominantly focus on single latent type such as point or grid latents. In contrast the proposed DITTO leverages both point and grid latents (i.e. dual latent) to enhance their strengths the stability of grid latents and the detail-rich capability of point latents. Concretely DITTO consists of dual latent encoder and integrated implicit decoder. In the dual latent encoder a dual latent layer which is the key module block composing the encoder refines both latents in parallel maintaining their distinct shapes and enabling recursive interaction. Notably a newly proposed dynamic sparse point transformer within the dual latent layer effectively refines point latents. Then the integrated implicit decoder systematically combines these refined latents achieving high-fidelity 3D reconstruction and surpassing previous state-of-the-art methods on object- and scene-level datasets especially in thin and detailed structures.
Single-Model and Any-Modality for Video Object Tracking
Authors: Jilai Zheng, Xiangxuan Ren, Florin-Alexandru Vasluianu, Chao Ma, Danda Pani Paudel, Luc Van Gool, Radu Timofte Zongwei Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the realm of video object tracking auxiliary modalities such as depth thermal or event data have emerged as valuable assets to complement the RGB trackers. In practice most existing RGB trackers learn a single set of parameters to use them across datasets and applications. However a similar single-model unification for multi-modality tracking presents several challenges. These challenges stem from the inherent heterogeneity of inputs -- each with modality-specific representations the scarcity of multi-modal datasets and the absence of all the modalities at all times. In this work we introduce Un-Track a Unified Tracker of a single set of parameters for any modality. To handle any modality our method learns their common latent space through low-rank factorization and reconstruction techniques. More importantly we use only the RGB-X pairs to learn the common latent space. This unique shared representation seamlessly binds all modalities together enabling effective unification and accommodating any missing modality all within a single transformer-based architecture. Our Un-Track achieves +8.1 absolute F-score gain on the DepthTrack dataset by introducing only +2.14 (over 21.50) GFLOPs with +6.6M (over 93M) parameters through a simple yet efficient prompting strategy. Extensive comparisons on five benchmark datasets with different modalities show that Un-Track surpasses both SOTA unified trackers and modality-specific counterparts validating our effectiveness and practicality. The source code is publicly available at https://github.com/Zongwei97/UnTrack.
FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking
Authors: Jiahui Huang, Seungryong Kim, Joon-Young Lee Seokju Cho
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In the domain of video tracking existing methods often grapple with a trade-off between spatial density and temporal range. Current approaches in dense optical flow estimators excel in providing spatially dense tracking but are limited to short temporal spans. Conversely recent advancements in long-range trackers offer extended temporal coverage but at the cost of spatial sparsity. This paper introduces FlowTrack a novel framework designed to bridge this gap. FlowTrack combines the strengths of both paradigms by 1) chaining confident flow predictions to maximize efficiency and 2) automatically switching to an error compensation module in instances of flow prediction inaccuracies. This dual strategy not only offers efficient dense tracking over extended temporal spans but also ensures robustness against error accumulations and occlusions common pitfalls of naive flow chaining. Furthermore we demonstrate that chained flow itself can serve as an effective guide for an error compensation module even for occluded points. Our framework achieves state-of-the-art accuracy for long-range tracking on the DAVIS dataset and renders 50% speed-up when performing dense tracking.
HIT: Estimating Internal Human Implicit Tissues from the Body Surface
Authors: Vaibhav Arora, Abdelmouttaleb Dakri, Shivam Chandhok, Jürgen Machann, Andreas Fritsche, Michael J. Black, Sergi Pujades Marilyn Keller
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The creation of personalized anatomical digital twins is important in the fields of medicine computer graphics sports science and biomechanics. To observe a subject's anatomy expensive medical devices (MRI or CT) are required and the creation of the digital model is often time-consuming and involves manual effort. Instead we leverage the fact that the shape of the body surface is correlated with the internal anatomy; e.g. from surface observations alone one can predict body composition and skeletal structure. In this work we go further and learn to infer the 3D location of three important anatomic tissues: subcutaneous adipose tissue (fat) lean tissue (muscles and organs) and long bones. To learn to infer these tissues we tackle several key challenges. We first create a dataset of human tissues by segmenting full-body MRI scans and registering the SMPL body mesh to the body surface. With this dataset we train HIT (Human Implicit Tissues) an implicit function that given a point inside a body predicts its tissue class. HIT leverages the SMPL body model shape and pose parameters to canonicalize the medical data. Unlike SMPL which is trained from upright 3D scans MRI scans are acquired with subjects lying on a table resulting in significant soft-tissue deformation. Consequently HIT uses a learned volumetric deformation field that undoes these deformations. Since HIT is parameterized by SMPL we can repose bodies or change the shape of subjects and the internal structures deform appropriately. We perform extensive experiments to validate HIT's ability to predict a plausible internal structure for novel subjects. The dataset and HIT model are available at https://hit.is.tue.mpg.de to foster future research in this direction.
DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance
Authors: Jia Jia, Shikun Sun, Haozhe Wu, Rong Han, Zhenyu Li, Di Tang, Jiaqing Zhou, Jiebo Luo Zixuan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Choreographers determine what the dances look like while cameramen determine the final presentation of dances. Recently various methods and datasets have showcased the feasibility of dance synthesis. However camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data. Thus we present DCM a new multi-modal 3D dataset which for the first time combines camera movement with dance motion and music audio. This dataset encompasses 108 dance sequences (3.2 hours) of paired dance-camera-music data from the anime community covering 4 music genres. With this dataset we uncover that dance camera movement is multifaceted and human-centric and possesses multiple influencing factors making dance camera synthesis a more challenging task compared to camera or dance synthesis alone. To overcome these difficulties we propose DanceCamera3D a transformer-based diffusion model that incorporates a novel body attention loss and a condition separation strategy. For evaluation we devise new metrics measuring camera movement quality diversity and dancer fidelity. Utilizing these metrics we conduct extensive experiments on our DCM dataset providing both quantitative and qualitative evidence showcasing the effectiveness of our DanceCamera3D model. Code and video demos are available at https://github.com/ Carmenw1203/DanceCamera3D-Official.
Synthesize Diagnose and Optimize: Towards Fine-Grained Vision-Language Understanding
Authors: Sicheng Xie, Zuyao You, Shiyi Lan, Zuxuan Wu Wujian Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However understanding fine-grained visual-linguistic concepts such as attributes and inter-object relationships remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity their primary focus remains on the linguistic aspect neglecting the visual dimension. Here we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine we carefully design a benchmark SPEC to diagnose the comprehension of object size position existence and count. Subsequently we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly their performance is close to random guess revealing significant limitations. With this in mind we propose a simple yet effective approach to optimize VLMs in fine-grained understanding achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements further validating the transferability of our approach. Code and data are available at https://github.com/wjpoom/SPEC.
Density-guided Translator Boosts Synthetic-to-Real Unsupervised Domain Adaptive Segmentation of 3D Point Clouds
Authors: Wankang Zeng, Yanfei Su, Weiquan Liu, Ming Cheng, Yulan Guo, Cheng Wang Zhimin Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task but its performance is limited by different sensor sampling patterns (i.e. variations in point density) and incomplete training strategies. In this work we propose a density-guided translator (DGT) which translates point density between domains and integrates it into a two-stage self-training pipeline named DGT-ST. First in contrast to existing works that simultaneously conduct data generation and feature/output alignment within unstable adversarial training we employ the non-learnable DGT to bridge the domain gap at the input level. Second to provide a well-initialized model for self-training we propose a category-level adversarial network in stage one that utilizes the prototype to prevent negative transfer. Finally by leveraging the designs above a domain-mixed self-training method with source-aware consistency loss is proposed in stage two to narrow the domain gap further. Experiments on two synthetic-to-real segmentation tasks (SynLiDAR ? semanticKITTI and SynLiDAR ? semanticPOSS) demonstrate that DGT-ST outperforms state-of-the-art methods achieving 9.4% and 4.3% mIoU improvements respectively. Code is available at https://github.com/yuan-zm/DGT-ST.
Cross Initialization for Face Personalization of Text-to-Image Models
Authors: Jian Yin, Haoran Xie, Qiping Wang, Qing Li, Xudong Mao Lianyu Pang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently there has been a surge in face personalization techniques benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these a notable method is Textual Inversion which generates personalized images by inverting given images into textual embeddings. However methods based on Textual Inversion still struggle with balancing the trade-off between reconstruction quality and editability. In this study we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. The scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting thereby compromising the editability. Driven by this observation we introduce a novel initialization method termed Cross Initialization that significantly narrows the gap between the initial and learned embeddings. This method not only improves both reconstruction and editability but also reduces the optimization steps from 5000 to 320. Furthermore we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably in our experiments Cross Initialization is the only method that successfully edits an individual's facial expression. Additionally a fast version of our method allows for capturing an input image in roughly 26 seconds while surpassing the baseline methods in terms of both reconstruction and editability. Code is available at https://github.com/lyuPang/CrossInitialization.
LEDITS++: Limitless Image Editing using Text-to-Image Models
Authors: Felix Friedrich, Katharia Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinario Passos Manuel Brack
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However existing image-to-image methods are often inefficient imprecise and of limited versatility. They either require time-consuming fine-tuning deviate unnecessarily strongly from the input image and/or lack support for multiple simultaneous edits. To address these issues we introduce LEDITS++ an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second our methodology supports multiple simultaneous edits and is architecture-agnostic. Third we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods.
Video Interpolation with Diffusion Models
Authors: Daniel Watson, Eric Tabellion, Aleksander Ho?ynski, Ben Poole, Janne Kontkanen Siddhant Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation and demonstrate how such works fail in most settings where the underlying motion is complex nonlinear or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the superresolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated requires less than a billion parameters per diffusion model to produce compelling results and still enjoys scalability and improved quality at larger parameter counts. Please see our project page at vidiminterpolation.github.io.
WildlifeMapper: Aerial Image Analysis for Multi-Species Detection and Identification
Authors: Bowen Zhang, Chandrakanth Gudavalli, Connor Levenson, Lacey Hughey, Jared A. Stabach, Irene Amoke, Gordon Ojwang, Joseph Mukeka, Stephen Mwiu, Joseph Ogutu, Howard Frederick, B.S. Manjunath Satish Kumar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce WildlifeMapper (WM) a flexible model designed to detect locate and identify multiple species in aerial imagery. It addresses the limitations of traditional labor-intensive wildlife population assessments that are central to advancing environmental conservation efforts worldwide. While a number of methods exist to automate this process they are often limited in their ability to generalize to different species or landscapes due to the dominance of homogeneous backgrounds and/or poorly captured local image structures. WM introduces two novel modules that help to capture the local structure and context of objects of interest to accurately localize and identify them achieving a state-of-the-art (SOTA) detection rate of 0.56 mAP. Further we introduce a large aerial imagery dataset with more than 11k Images and 28k annotations verified by trained experts. WM also achieves SOTA performance on 3 other publicly available aerial survey datasets collected across 4 different countries improving mAP by 42%. Source code and trained models are available at Github
Learning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation
Authors: Jianman Lin, Zhijing Yang, Chunmei Qing, Liang Lin Tianshui Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person where two aligned frames exhibit the same speech content yet differ in emotional expression limiting the SPFEM applications in real-world scenarios. In this work we discover that speakers who convey the same content with different emotions exhibit highly correlated local facial animations providing valuable supervision for SPFEM. To capitalize on this insight we propose a novel adaptive spatial coherent correlation learning (ASCCL) algorithm which models the aforementioned correlation as an explicit metric and integrates the metric to supervise manipulating facial expression and meanwhile better preserving the facial animation of spoken contents. To this end it first learns a spatial coherent correlation metric ensuring the visual disparities of adjacent local regions of the image belonging to one emotion are similar to those of the corresponding counterpart of the image belonging to another emotion. Recognizing that visual disparities are not uniform across all regions we have also crafted a disparity-aware adaptive strategy that prioritizes regions that present greater challenges. During SPFEM model training we construct the adaptive spatial coherent correlation metric between corresponding local regions of the input and output images as addition loss to supervise the generation process. We conduct extensive experiments on variant datasets and the results demonstrate the effectiveness of the proposed ASCCL algorithm. Code is publicly available at https://github.com/jianmanlincjx/ASCCL
Tune-An-Ellipse: CLIP Has Potential to Find What You Want
Authors: Songhe Deng, Bing Li, Haozhe Liu, Yawen Huang, Yefeng Zheng, Jurgen Schmidhuber, Bernard Ghanem, Linlin Shen, Mike Zheng Shou Jinheng Xie
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities. A manually drawn red circle commonly used for highlighting can guide CLIP's attention to the surrounding region to identify specific objects within an image. Without precise object proposals however it is insufficient for localization. Our novel simple yet effective approach i.e. Differentiable Visual Prompting enables CLIP to zero-shot localize: given an image and a text prompt describing an object we first pick a rendered ellipse from uniformly distributed anchor ellipses on the image grid via visual prompting then use three loss functions to tune the ellipse coefficients to encapsulate the target region gradually. This yields promising experimental results for referring expression comprehension without precisely specified object proposals. In addition we systematically present the limitations of visual prompting inherent in CLIP and discuss potential solutions.
Neural Spline Fields for Burst Image Fusion and Layer Separation
Authors: David Shustin, Ruyu Yan, Chenyang Lei, Felix Heide Ilya Chugunov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax diffuse and specular materials scene motion and illuminant variation. While decomposing all of these effects from a stack of misaligned images is a highly ill-conditioned task the conventional align-and-merge burst pipeline takes the other extreme: blending them into a single image. In this work we propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields -- networks trained to map input coordinates to spline control points. Our method is able to during test-time optimization jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers. Then by discarding the obstruction layer we can perform a range of tasks including seeing through occlusions reflection suppression and shadow removal. Tested on complex in-the-wild captures we find that with no post-processing steps or learned priors our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches.
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
Authors: Juyong Kim, Eni Halilaj, Michael J. Black Soyong Shin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The estimation of 3D human motion from video has progressed rapidly but current methods still have several key limitations. First most methods estimate the human in camera coordinates. Second prior work on estimating humans in global coordinates often assumes a flat ground plane and produces foot sliding. Third the most accurate methods rely on computationally expensive optimization pipelines limiting their use to offline applications. Finally existing video-based methods are surprisingly less accurate than single-frame methods. We address these limitations with WHAM (World-grounded Humans with Accurate Motion) which accurately and efficiently reconstructs 3D human motion in a global coordinate system from video. WHAM learns to lift 2D keypoint sequences to 3D using motion capture data and fuses this with video features integrating motion context and visual information. WHAM exploits camera angular velocity estimated from a SLAM method together with human motion to estimate the body's global trajectory. We combine this with a contact-aware trajectory refinement method that lets WHAM capture human motion in diverse conditions such as climbing stairs. WHAM outperforms all existing 3D human motion recovery methods across multiple in-the-wild benchmarks. Code is available for research purposes at http://wham.is.tue.mpg.de/.
NAPGuard: Towards Detecting Naturalistic Adversarial Patches
Authors: Jiakai Wang, Jiejie Zhao, Yazhe Wang, Xianglong Liu Siyang Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently the emergence of naturalistic adversarial patch (NAP) which possesses a deceptive appearance and various representations underscores the necessity of developing robust detection strategies. However existing approaches fail to differentiate the deep-seated natures in adversarial patches i.e. aggressiveness and naturalness leading to unsatisfactory precision and generalization against NAPs. To tackle this issue we propose NAPGuard to provide strong detection capability against NAPs via the elaborated critical feature modulation framework. For improving precision we propose the aggressive feature aligned learning to enhance the model's capability in capturing accurate aggressive patterns. Considering the challenge of inaccurate model learning caused by deceptive appearance we align the aggressive features by the proposed pattern alignment loss during training. Since the model could learn more accurate aggressive patterns it is able to detect deceptive patches more precisely. To enhance generalization we design the natural feature suppressed inference to universally mitigate the disturbance from different NAPs. Since various representations arise in diverse disturbing forms to hinder generalization we suppress the natural features in a unified approach via the feature shield module. Therefore the models could recognize NAPs within less disturbance and activate the generalized detection ability. Extensive experiments show that our method surpasses state-of-the-art methods by large margins in detecting NAPs (improve 60.24% AP@0.5 on average).
DiffPerformer: Iterative Learning of Consistent Latent Guidance for Diffusion-based Human Video Generation
Authors: Zerong Zheng, Tao Yu, Xiaoqian Lv, Bineng Zhong, Shengping Zhang, Liqiang Nie Chenyang Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing diffusion models for pose-guided human video generation mostly suffer from temporal inconsistency in the generated appearance and poses due to the inherent randomization nature of the generation process. In this paper we propose a novel framework DiffPerformer to synthesize high-fidelity and temporally consistent human video. Without complex architecture modification or costly training DiffPerformer finetunes a pretrained diffusion model on a single video of the target character and introduces an implicit video representation as a proxy to learn temporally consistent guidance for the diffusion model. The guidance is encoded into VAE latent space and an iterative optimization loop is constructed between the implicit video representation and the diffusion model allowing to harness the smooth property of the implicit video representation and the generative capabilities of the diffusion model in a mutually beneficial way. Moreover we propose 3D-aware human flow as a temporal constraint during the optimization to explicitly model the correspondence between driving poses and human appearance. This alleviates the misalignment between guided poses and target performer and therefore maintains the appearance coherence under various motions. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods.