4 papers
Seeing Isn't Believing: Uncovering Blind Spots in Evaluator Vision-Language Models
Mohammed Safi Ur Rahman Khan, Sanjay Suryanarayanan, Tushar Anand +1
Large Vision-Language Models (VLMs) are increasingly used to evaluate outputs of other models, for image-to-text (I2T) tasks such as visual question answering, and text-to-image (T…
DenVisCoM: Dense Vision Correspondence Mamba for Efficient and Real-time Optical Flow and Stereo Estimation
Tushar Anand, Maheswar Bora, Antitza Dantcheva +1
In this work, we propose a novel Mamba block DenVisCoM, as well as a novel hybrid architecture specifically tailored for accurate and real-time estimation of optical flow and dispa…
DensePercept-NCSSD: Vision Mamba towards Real-time Dense Visual Perception with Non-Causal State Space Duality
Tushar Anand, Advik Sinha, Abhijit Das
In this work, we propose an accurate and real-time optical flow and disparity estimation model by fusing pairwise input images in the proposed non-causal selective state space for…
ViM-Disparity: Bridging the Gap of Speed, Accuracy and Memory for Disparity Map Generation
Maheswar Bora, Tushar Anand, Saurabh Atreya +2
In this work we propose a Visual Mamba (ViM) based architecture, to dissolve the existing trade-off for real-time and accurate model with low computation overhead for disparity map…