(MBZUAI, Computer Vision)
My research is focused on developing multi-modal understanding from vision and text to improve common-sense reasoning of machines and its applications.
I received my B.Sc. degreen in Electrical Enginerring from UET Lahore with honors in 2018. After my graduation I joined Confiz Limited as Computer Vision engineer where I worked on design and deployment of deep-learning driven computer vision solutions for retail industry. In December 2022, I completed my MS.c. degree in Computer Vision from MBZUAI.
*Hanoona Rasheed, *Muhammad Maaz, Sahal Shaji, Abdelrahman Shaker, Salman Khan, Hisham Cholakkal, Rao M. Anwer, Eric Xing, Ming-Hsuan Yang, Fahad S. Khan
Grounding Large Multimodal Model (GLaMM) is an end-to-end trained LMM which provides visual grounding capabilities with the flexibility to process both image and region inputs. This enables the new unified task of Grounded Conversation Generation that combines phrase grounding, referring expression segmentation and vision-language conversations. Equipped with the capability for detailed region understanding, pixel-level groundings, and conversational abilities, GLaMM offers a versatile capability to interact with visual inputs provided by the user at multiple granularity levels (objects, object parts, attributes, relationships and holistic scene understanding).
*Muhammad Maaz, *Hanoona Rasheed, Salman Khan, Fahad Khan
Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. It also presents the first quantitative benchmarks to evaluate video-based conversational models.
*Muhammad Maaz, *Hanoona Rasheed, Salman Khan, Fahad Khan, Rao M.Anwer, Ming-Hsuan Yang
In this work, we explore the potential of the recent Multi-modal Vision Transformers (MViTs) for class-agnostic object detection. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. We also develop an efficient and flexible MViT architecture using multi-scale feature processing and deformable self-attention that can adaptively generate proposals given a specific language query.
Open-Vocabulary Detection [NeurIPS-2022]
*Hanoona Rasheed, *Muhammad Maaz, M. Uzair Khattak, Salman Khan, Fahad Khan
In this work, we propose to solve the Open-vocabulary detection (OVD) problem using pretrained CLIP model, adapting it for object-centric local regions using region-based distillation and image-level weak supervision. Specifically, we propose to utilize high-quality class-agnostic and class-specific object proposals via the pretrained mulit-modal vision transformers (MViT). The class-agnostic proposals are used to distill region-specific information from CLIP and class-specific proposals allows us to visually ground large vocabularies. We also introduce a region-conditioned weight transfer method to get complementary benefits from both region-based distillation and image-level supervision.
Hanoona Rasheed, M. Uzair Khattak, Muhammad Maaz, Salman Khan, Fahad Khan
In this work, we show that a simple Video Fine-tuned CLIP (ViFi-CLIP) baseline is generally sufficient to bridge the domain gap from images to videos. Our qualitative analysis illustrates that the frame-level processing from CLIP image-encoder followed by feature pooling and similarity matching with corresponding text embeddings helps in implicitly modeling the temporal cues within ViFi-CLIP. Such fine-tuning helps the model to focus on scene dynamics, moving objects and inter-object relationships. For low-data regimes where full fine-tuning is not viable, we propose a ‘bridge and prompt’ approach that first uses fine-tuning to bridge the domain gap and then learns prompts on language and vision side to adapt CLIP representations.
M. Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, Fahad Khan
In this work, we propose to learn prompts in both vision and language branches of pretrained CLIP for adapting it to different downstream tasks. Previous works only use prompting in either language or vision branch. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. To this end, we propose Multi-modal Prompt Learning (MaPLe) for both vision and language branches to improve alignment between the vision and language representations. Our design promotes strong coupling between the vision-language prompts to ensure mutual synergy and discourages learning independent uni-modal solutions.
*Muhammad Maaz, *Abdelrahman Shaker, Hisham Cholakkal, Salman Khan, S.~Waqas Zamir, Rao M.~Anwer, Fahad Khan
In this work, we designed resource-efficient general purpose backbone network for vision tasks. We combine the strengths of both CNN and Transformer models and propose a new efficient hybrid architecture EdgeNeXt. Specifically in EdgeNeXt, we introduce split depth-wise transpose attention (SDTA) encoder that splits input tensors into multiple channel groups and utilizes depth-wise convolution along with self-attention across channel dimensions to implicitly increase the receptive field and encode multi-scale features. Our extensive experiments on classification, detection and segmentation tasks, reveal the merits of the proposed approach, outperforming state-of-the-art methods with comparatively lower compute requirements.
Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad S. Khan
The work proposes a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters and compute cost. Our extensive evaluations on three benchmarks, Synapse, BTCV and ACDC, reveal the effectiveness of the proposed contributions in terms of both efficiency and accuracy.
In this work, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters and compute cost. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient having linear complexity with respect to the input sequence length. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the overall network parameters.