GenAI4ELab/papercli-papers huggingface dataset:
Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion
Authors: Naishan Zheng; Man Zhou; Jie Huang; Junming Hou; Haoying Li; Yuan Xu; Feng Zhao
Venue: CVPR 2024 (main)

Abstract: Infrared and visible image fusion aims to generate a fused image by integrating and distinguishing complementary information from multiple sources. While the cross-attention mechanism with global spatial interactions appears promising it only capture second-order spatial interactions neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the exploitation of synergies between multi-modalities. To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility. 2) Channel dimension: expanding on channel interactions with first-order statistics (mean) we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies leading in superior performance over state-of-the-art alternatives as shown through comprehensive experiments across various benchmarks.
VideoLLM-online: Online Video Large Language Model for Streaming Video
Authors: Joya Chen; Zhaoyang Lv; Shiwei Wu; Kevin Qinghong Lin; Chenan Song; Difei Gao; Jia-Wei Liu; Ziteng Gao; Dongxing Mao; Mike Zheng Shou
Venue: CVPR 2024 (main)

Abstract: Large Language Models (LLMs) have been enhanced with vision capabilities enabling them to comprehend images videos and interleaved vision-language content. However the learning methods of these large multimodal models (LMMs) typically treat videos as predetermined clips rendering them less effective and efficient at handling streaming video inputs. In this paper we propose a novel Learning-In-Video-Stream (LIVE) framework which enables temporally aligned long-context and real-time dialogue within a continuous video stream. Our LIVE framework comprises comprehensive approaches to achieve video streaming dialogue encompassing: (1) a training objective designed to perform language modeling for continuous streaming inputs (2) a data generation scheme that converts offline temporal annotations into a streaming dialogue format and (3) an optimized inference pipeline to speed up interactive chat in real-world video streams. With our LIVE framework we develop a simplified model called VideoLLM-online and demonstrate its significant advantages in processing streaming videos. For instance our VideoLLM-online-7B model can operate at over 10 FPS on an A100 GPU for a 5-minute video clip from Ego4D narration. Moreover VideoLLM-online also showcases state-of-the-art performance on public offline video benchmarks such as recognition captioning and forecasting. The code model data and demo have been made available at showlab.github.io/videollm-online.
Text-conditional Attribute Alignment across Latent Spaces for 3D Controllable Face Image Synthesis
Authors: Feifan Xu; Rui Li; Si Wu; Yong Xu; Hau San Wong
Venue: CVPR 2024 (main)

Abstract: With the advent of generative models and vision language pretraining significant improvement has been made in text-driven face manipulation. The text embedding can be used as target supervision for expression control.However it is non-trivial to associate with its 3D attributesi.e. pose and illumination. To address these issues we propose a Text-conditional Attribute aLignment approach for 3D controllable face image synthesis and our model is referred to as TcALign. Specifically since the 3D rendered image can be precisely controlled with the 3D face representation we first propose a Text-conditional 3D Editor to produce the target face representation to realize text-driven manipulation in the 3D space. An attribute embedding space spanned by the target-related attributes embeddings is also introduced to infer the disentangled task-specific direction. Next we train a cross-modal latent mapping network conditioned on the derived difference of 3D representation to infer a correct vector in the latent space of StyleGAN.Thiscorrection vector learning design can accurately transfer the attribute manipulation on 3D images to 2D images. We show that the proposed method delivers more precise text-driven multi-attribute manipulation for 3D controllable face image synthesis. Extensive qualitative and quantitative experiments verify the effectiveness and superiority of our method over the other competing methods.
ESCAPE: Encoding Super-keypoints for Category-Agnostic Pose Estimation
Authors: Khoi Duc Nguyen; Chen Li; Gim Hee Lee
Venue: CVPR 2024 (main)

Abstract: In this paper we tackle the task of category-agnostic pose estimation (CAPE) which aims to predict poses for objects of any category with few annotated samples. Previous works either rely on local matching between features of support and query samples or require support keypoint identifier. The former is prone to overfitting due to its sensitivity to sparse samples while the latter is impractical for the open-world nature of the task. To overcome these limitations we propose ESCAPE - a Bayesian framework that learns a prior over the features of keypoints. The prior can be expressed as a mixture of super-keypoints each being a high-level abstract keypoint that captures the statistics of semantically related keypoints from different categories. We estimate the super-keypoints from base categories and use them in adaptation to novel categories. The adaptation to an unseen category involves two steps: first we match each novel keypoint to a related super-keypoint; and second we transfer the knowledge encoded in the matched super-keypoints to the novel keypoints. For the first step we propose a learnable matching network to capture the relationship between the novel keypoints and the super-keypoints resulting in a more reliable matching. ESCAPE mitigates overfitting by directly transferring learned knowledge to novel categories while it does not use keypoint identifiers. We achieve state-of-the-art performance on the standard MP-100 benchmark.
Correcting Diffusion Generation through Resampling
Authors: Yujian Liu; Yang Zhang; Tommi Jaakkola; Shiyu Chang
Venue: CVPR 2024 (main)

Abstract: Despite diffusion models' superior capabilities in modeling complex distributions there are still non-trivial distributional discrepancies between generated and ground-truth images which has resulted in several notable problems in image generation including missing object errors in text-to-image generation and low image quality. Existing methods that attempt to address these problems mostly do not tend to address the fundamental cause behind these problems which is the distributional discrepancies and hence achieve sub-optimal results. In this paper we propose a particle filtering framework that can effectively address both problems by explicitly reducing the distributional discrepancies. Specifically our method relies on a set of external guidance including a small set of real images and a pre-trained object detector to gauge the distribution gap and then design the resampling weight accordingly to correct the gap. Experiments show that our methods can effectively correct missing object errors and improve image quality in various image generation tasks. Notably our method outperforms the existing strongest baseline by 5% in object occurrence and 1.0 in FID on MS-COCO. Our code is available at https://github.com/UCSB-NLP-Chang/diffusion_resampling.git.
Towards Better Vision-Inspired Vision-Language Models
Authors: Yun-Hao Cao; Kaixiang Ji; Ziyuan Huang; Chuanyang Zheng; Jiajia Liu; Jian Wang; Jingdong Chen; Ming Yang
Venue: CVPR 2024 (main)

Abstract: Vision-language (VL) models have achieved unprecedented success recently in which the connection module is the key to bridge the modality gap. Nevertheless the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side most existing approaches only use the last feature of the vision tower without using the low-level features. On the language side most existing methods only introduce shallow vision-language interactions. In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models. To take advantage of the lowerlevel information from the vision tower a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of-the-art method by 18.1 CIDEr when training from scratch on the COCO caption task which greatly improves the data efficiency. When used as a plug-in module VIVL consistently improves the performance for various backbones and VL frameworks delivering new state-of-the-art results on multiple benchmarks e.g. NoCaps and VQAv2.
VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection
Authors: Zihua Liu; Hiroki Sakuma; Masatoshi Okutomi
Venue: CVPR 2024 (main)

Abstract: Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abundant 3D labels typically obtained through expensive and labor-intensive annotation on LiDAR point clouds. To tackle this problem we propose a novel weakly supervised 3D object detection framework named VSRD (Volumetric Silhouette Rendering for Detection) to train 3D object detectors without any 3D supervision but only weak 2D supervision. VSRD consists of multi-view 3D auto-labeling and subsequent training of monocular 3D object detectors using the pseudo labels generated in the auto-labeling stage. In the auto-labeling stage we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instance-aware volumetric silhouette rendering. To directly optimize the 3D bounding boxes through rendering we decompose the SDF of each instance into the SDF of a cuboid and the residual distance field (RDF) that represents the residual from the cuboid. This mechanism enables us to optimize the 3D bounding boxes in an end-to-end manner by comparing the rendered instance masks with the ground truth instance masks. The optimized 3D bounding boxes serve as effective training data for 3D object detection. We conduct extensive experiments on the KITTI-360 dataset demonstrating that our method outperforms the existing weakly supervised 3D object detection methods. The code is available at https://github.com/skmhrk1209/VSRD.
RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation
Authors: Zeyuan Yang; Jiageng Liu; Peihao Chen; Anoop Cherian; Tim K. Marks; Jonathan Le Roux; Chuang Gan
Venue: CVPR 2024 (main)

Abstract: We leverage Large Language Models (LLM) for zeroshot Semantic Audio Visual Navigation (SAVN). Existing methods utilize extensive training demonstrations for reinforcement learning yet achieve relatively low success rates and lack generalizability. The intermittent nature of auditory signals further poses additional obstacles to inferring the goal information. To address this challenge we present the Reflective and Imaginative Language Agent (RILA). By employing multi-modal models to process sensory data we instruct an LLM-based planner to actively explore the environment. During the exploration our agent adaptively evaluates and dismisses inaccurate perceptual descriptions. Additionally we introduce an auxiliary LLMbased assistant to enhance global environmental comprehension by mapping room layouts and providing strategic insights. Through comprehensive experiments and analysis we show that our method outperforms relevant baselines without training demonstrations from the environment and complementary semantic information.
Endow SAM with Keen Eyes: Temporal-spatial Prompt Learning for Video Camouflaged Object Detection
Authors: Wenjun Hui; Zhenfeng Zhu; Shuai Zheng; Yao Zhao
Venue: CVPR 2024 (main)

Abstract: The Segment Anything Model (SAM) a prompt-driven foundational model has demonstrated remarkable performance in natural image segmentation. However its application in video camouflaged object detection (VCOD) encounters challenges chiefly stemming from the overlooked temporal-spatial associations and the unreliability of user-provided prompts for camouflaged objects that are difficult to discern with the naked eye. To tackle the above issues we endow SAM with keen eyes and propose the Temporal-spatial Prompt SAM (TSP-SAM) a novel approach tailored for VCOD via an ingenious prompted learning scheme. Firstly motion-driven self-prompt learning is employed to capture the camouflaged object thereby bypassing the need for user-provided prompts. With the detected subtle motion cues across consecutive video frames the overall movement of the camouflaged object is captured for more precise spatial localization. Subsequently to eliminate the prompt bias resulting from inter-frame discontinuities the long-range consistency within the video sequences is taken into account to promote the robustness of the self-prompts. It is also injected into the encoder of SAM to enhance the representational capabilities. Extensive experimental results on two benchmarks demonstrate that the proposed TSP-SAM achieves a significant improvement over the state-of-the-art methods. With the mIoU metric increasing by 7.8% and 9.6% TSP-SAM emerges as a groundbreaking step forward in the field of VCOD.
TULIP: Multi-camera 3D Precision Assessment of Parkinson's Disease
Authors: Kyungdo Kim; Sihan Lyu; Sneha Mantri; Timothy W. Dunn
Venue: CVPR 2024 (main)

Abstract: Parkinson's disease (PD) is a devastating movement disorder accelerating in global prevalence but a lack of precision symptom measurement has made the development of effective therapies challenging. The Unified Parkinson's Disease Rating Scale (UPDRS) is the gold-standard for assessing motor symptom severity yet its manual scoring criteria are vague and subjective resulting in coarse and noisy clinical assessments. Machine learning approaches have the potential to modernize PD symptom assessments by making them more quantitative objective and scalable. However the lack of benchmark video datasets for PD motor exams hinders model development. Here we introduce the TULIP dataset to bridge this gap. TULIP emphasizes precision and comprehensiveness comprising multi-view video recordings (6 cameras) of all 25 UPDRS motor exam components together with ratings by 3 clinical experts in a cohort of Parkinson's patients and healthy controls. The multi-view recordings enable 3D reconstructions of body movement that better capture disease signatures than more conventional 2D methods. Using the dataset we establish a baseline model for predicting UPDRS scores from 3D poses illustrating how existing diagnostics could be automated. Looking ahead TULIP could aid the development of new precision diagnostics that transcend UPDRS scores providing a deeper understanding of PD and its potential treatments.
HybridNeRF: Efficient Neural Rendering via Adaptive Volumetric Surfaces
Authors: Haithem Turki; Vasu Agrawal; Samuel Rota Bulò; Lorenzo Porzi; Peter Kontschieder; Deva Ramanan; Michael Zollhöfer; Christian Richardt
Venue: CVPR 2024 (main)

Abstract: Neural radiance fields provide state-of-the-art view synthesis quality but tend to be slow to render. One reason is that they make use of volume rendering thus requiring many samples (and model queries) per ray at render time. Although this representation is flexible and easy to optimize most real-world objects can be modeled more efficiently with surfaces instead of volumes requiring far fewer samples per ray. This observation has spurred considerable progress in surface representations such as signed distance functions but these may struggle to model semi-opaque and thin structures. We propose a method HybridNeRF that leverages the strengths of both representations by rendering most objects as surfaces while modeling the (typically) small fraction of challenging regions volumetrically. We evaluate HybridNeRF against the challenging Eyeful Tower dataset along with other commonly used view synthesis datasets. When comparing to state-of-the-art baselines including recent rasterization-based approaches we improve error rates by 15-30% while achieving real-time framerates (at least 36 FPS) for virtual-reality resolutions (2K x 2K).
AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
Authors: Jamie Watson; Filippo Aleotti; Mohamed Sayed; Zawar Qureshi; Oisin Mac Aodha; Gabriel Brostow; Michael Firman; Sara Vicente
Venue: CVPR 2024 (main)

Abstract: Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However such purely geometric methods are understandably oblivious to plane semantics which are crucial to discerning distinct planes. To overcome this limitation we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
Authors: Huan Liu; Zichang Tan; Chuangchuang Tan; Yunchao Wei; Jingdong Wang; Yao Zhao
Venue: CVPR 2024 (main)

Abstract: In this paper we study the problem of generalizable synthetic image detection aiming to detect forgery images from diverse generative methods e.g. GANs and diffusion models. Cutting-edge solutions start to explore the benefits of pre-trained models and mainly follow the fixed paradigm of solely training an attached classifier e.g. combining frozen CLIP-ViT with a learnable linear layer in UniFD. However our analysis shows that such a fixed paradigm is prone to yield detectors with insufficient learning regarding forgery representations. We attribute the key challenge to the lack of forgery adaptation and present a novel forgery-aware adaptive transformer approach namely FatFormer. Based on the pre-trained vision-language spaces of CLIP FatFormer introduces two core designs for the adaption to build generalized forgery representations. First motivated by the fact that both image and frequency analysis are essential for synthetic image detection we develop a forgery-aware adapter to adapt image features to discern and integrate local forgery traces within image and frequency domains. Second we find that considering the contrastive objectives between adapted image features and text prompt embeddings a previously overlooked aspect results in a nontrivial generalization improvement. Accordingly we introduce language-guided alignment to supervise the forgery adaptation with image and text prompts in FatFormer. Experiments show that by coupling these two designs our approach tuned on 4-class ProGAN data attains a remarkable detection performance achieving an average of 98% accuracy to unseen GANs and surprisingly generalizes to unseen diffusion models with 95% accuracy.
PostureHMR: Posture Transformation for 3D Human Mesh Recovery
Authors: Yu-Pei Song; Xiao Wu; Zhaoquan Yuan; Jian-Jun Qiao; Qiang Peng
Venue: CVPR 2024 (main)

Abstract: Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.
Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains
Authors: Bang-Dang Pham; Phong Tran; Anh Tran; Cuong Pham; Rang Nguyen; Minh Hoai
Venue: CVPR 2024 (main)

Abstract: This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image which is challenging to deblur into another blurry image that is more amenable to deblurring. The transformation process from one blurry state to another leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://github.com/VinAIResearch/Blur2Blur
Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis
Authors: Xin Zhou; Dingkang Liang; Wei Xu; Xingkui Zhu; Yihan Xu; Zhikang Zou; Xiang Bai
Venue: CVPR 2024 (main)

Abstract: Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. However existing methods for model adaptation usually update all model parameters i.e. full fine-tuning paradigm which is inefficient as it relies on high computational costs (e.g. training GPU memory) and massive storage space. In this paper we aim to study parameter-efficient transfer learning for point cloud analysis with an ideal trade-off between task performance and parameter efficiency. To achieve this goal we freeze the parameters of the default pre-trained models and then propose the Dynamic Adapter which generates a dynamic scale for each token considering the token significance to the downstream task. We further seamlessly integrate Dynamic Adapter with Prompt Tuning (DAPT) by constructing Internal Prompts capturing the instance-specific features for interaction. Extensive experiments conducted on five challenging datasets demonstrate that the proposed DAPT achieves superior performance compared to the full fine-tuning counterparts while significantly reducing the trainable parameters and training GPU memory by 95% and 35% respectively. Code is available at https://github.com/LMD0311/DAPT.