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Brightness constancy and small-motion assumptions
Brightness constancy says a point's intensity is invariant under motion; small motion lets you linearize via Taylor expansion.
Kalman filter for bounding-box tracking
State, transition, measurement models, and process plus measurement noise; predict then correct each frame. State holds box position and velocity; measurement is the detected box.
3D CNNs vs two-stream action recognition
3D CNNs learn spatiotemporal filters end to end but are heavy; two-stream splits RGB appearance and precomputed optical flow, strong but costly to compute flow.
Re-identification in multi-object tracking
Re-ID matches an object to its prior id using appearance embeddings, not just position; store track features and match re-entering detections by embedding similarity.
Self-supervised pretraining for video understanding
Define a label-free task like temporal order prediction or contrastive clip matching that forces temporal reasoning, then fine-tune on labeled action data.
Scene flow versus optical flow
Optical flow is 2D pixel motion in the image plane; scene flow is the 3D motion field of points in space, needing depth via stereo, RGB-D, or LiDAR.
How ViT and CNN process an image differently
A CNN slides local filters over the raw pixel grid; a ViT splits the image into patches, flattens and linearly embeds each into a token, adds positional embeddings, and feeds the sequence to…
Self-attention over image patches explained
Each patch projects to query, key, value; a patch's query is scored against all keys, softmax-normalized into weights, used to combine all values.
Inductive biases of ViT versus CNN
CNNs bake in locality and translation equivariance; a plain ViT has almost none beyond patch structure, so it must learn spatial relations from data, needing large datasets or strong pretraining.
How Swin Transformer achieves linear attention
Swin computes attention within local non-overlapping windows of fixed size, making cost linear in patches, then shifts windows between layers so information crosses boundaries.
Cross-attention for visual question answering
In cross-attention queries come from one modality and keys/values from the other, e.g. text queries attend over image features so the question selects relevant regions.
Why ViTs need positional embeddings
Self-attention is permutation invariant so patch order is lost; positional embeddings restore spatial location. CNNs encode position implicitly via the fixed convolution grid.
Core principles of a Neural Radiance Field
An MLP maps a 3D point plus view direction to color and density; novel views render by casting rays, sampling points, querying the MLP, and volume-integrating along each ray.
Pure ViT vs hybrid CNN-Transformer for medical segmentation
Pure ViT captures global context but is data hungry and weak on local detail; hybrid CNN-Transformer gets local features cheaply plus global attention, ideal for scarce…
Attention in diffusion U-Nets for text conditioning
Self-attention mixes spatial features at low-res blocks; cross-attention has image queries attend to text-token keys/values; placed inside transformer blocks.
GAN architecture: generator and discriminator roles
Generator maps noise to fake samples, discriminator classifies real versus fake, they train as a two-player game until samples fool the discriminator.
Diffusion forward and reverse processes
Forward process gradually adds Gaussian noise until data is pure noise; reverse process learns to denoise step by step; the network predicts the noise added at each timestep.
Mode collapse in GAN training
Generator produces few outputs ignoring data diversity, caused by chasing whatever fools the current discriminator; mitigate with minibatch discrimination, unrolled GANs, or Wasserstein loss.
Evaluating generative models with FID versus IS
FID compares Inception feature distributions of real and fake images via Frechet distance between two Gaussians; it uses real data as reference and detects diversity issues, unlike IS which uses no real…
Why U-Net skip connections matter for denoising
Skips carry high-resolution spatial detail from encoder to decoder, preserving fine structure lost in downsampling and easing gradient flow, which lets the model restore detail while removing…